Publications - PhenoRob

2025

P. M. Blok, F. Magistri, C. Stachniss, H. Wang, J. Burridge, and W. Guo, “High-Throughput 3D Shape Completion of Potato Tubers on a Harvester,” , vol. 228, p. 109673, 2025. doi:https://doi.org/10.1016/j.compag.2024.109673
[BibTeX] [PDF]

@article{blok2025cea,
author = {P.M. Blok and F. Magistri and C. Stachniss and H. Wang and J. Burridge and W. Guo},
title = {{High-Throughput 3D Shape Completion of Potato Tubers on a Harvester}},
journal = cea,
year = 2025,
volume = {228},
pages = {109673},
doi = {https://doi.org/10.1016/j.compag.2024.109673},
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/blok2025cea.pdf},
}

A. Dreier, A. Tobies, H. Kuhlmann, and L. Klingbeil, “Stone instance segmentation of rubble masonry based on laser scanning point clouds,” Measurement, vol. 242, p. 115905, 2025. doi:10.1016/j.measurement.2024.115905
[BibTeX] [PDF]

Laser scanning allows for objective area-based analysis of water dam structures to enable targeted interventions in case of displacements, requiring comparison of the same areas over different epochs. This comparison improves if identical areas, e.g. stones, from multiple epochs can be automatically derived in advance. We present an instance segmentation algorithm based on a 3D point cloud with reflected intensity information to identify individual stones in rubble masonry dams that can be used within deformation monitoring. The algorithm uses initial k-means classification followed by image-based processing steps, resulting in a segmented 3D point cloud with individual stones. It is evaluated against a manually labeled reference and analyzed on terrestrial laser scanning (TLS) and UAV-based data sets. Correctly identified stones are 89.92% for TLS and 90.82% for UAV data. Additionally, stone centroids and shapes were evaluated without significant deviation. A first outlook on deformation analysis was provided using ICP matching of stones from two simulated epochs.

@article{DREIER2025115905,
title = {Stone instance segmentation of rubble masonry based on laser scanning point clouds},
journal = {Measurement},
volume = {242},
pages = {115905},
year = {2025},
issn = {0263-2241},
doi = {10.1016/j.measurement.2024.115905},
url = {https://www.sciencedirect.com/science/article/pii/S0263224124017901},
author = {A. Dreier and A. Tobies and H. Kuhlmann and L. Klingbeil},
keywords = {Deformation monitoring, ICP, TLS, UAV-based laser scanning, Intensity, Reflectance},
abstract = {Laser scanning allows for objective area-based analysis of water dam structures to enable targeted interventions in case of displacements, requiring comparison of the same areas over different epochs. This comparison improves if identical areas, e.g. stones, from multiple epochs can be automatically derived in advance. We present an instance segmentation algorithm based on a 3D point cloud with reflected intensity information to identify individual stones in rubble masonry dams that can be used within deformation monitoring. The algorithm uses initial k-means classification followed by image-based processing steps, resulting in a segmented 3D point cloud with individual stones. It is evaluated against a manually labeled reference and analyzed on terrestrial laser scanning (TLS) and UAV-based data sets. Correctly identified stones are 89.92% for TLS and 90.82% for UAV data. Additionally, stone centroids and shapes were evaluated without significant deviation. A first outlook on deformation analysis was provided using ICP matching of stones from two simulated epochs.}
}

D. T. Demie, D. Wallach, T. F. Döring, F. Ewert, T. Gaiser, S. Hadir, G. Krauss, M. Paul, I. M. Hernández-Ochoa, R. Vezy, and S. J. Seidel, “Evaluating a new intercrop model for capturing mixture effects with an extensive intercrop dataset,” Agriculture, Ecosystems & Environment, vol. 378, p. 109302, 2025. doi:10.1016/j.agee.2024.109302
[BibTeX] [PDF]

Cereal-legume intercrops have numerous advantages over monocultures. However, the intercrop’s performance depends on the plant genotypes, management, and environment. Process-based agro-ecosystem models are important tools to evaluate the performance of intercrop systems as field experiments are limited in the number of treatments. The objective of this study was to calibrate and evaluate a new process-based intercrop model using an extensive experimental data set and to test whether the model is suitable for comparing intercrop management strategies. The data set includes all combinations of 12 different spring wheat entries (SW, Triticum aestivum L.) with two faba bean (FB, Vicia faba L.) cultivars, at two sowing densities, in three different environments. The results show that the intercrop model was capable of simulating the absolute mixture (intercrop) effects (AME) for grain yield, above-ground biomass, and topsoil root biomass, for both crops. However, the intercrop model does not perform better than a benchmark that ignores the intercrop effects when simulating plant height, fraction of intercepted radiation, volumetric soil water content, and subsoil root biomass. The intercrop model predicted reasonably well the differences between species and between SW cultivars for grain yield and aboveground plant biomass. Overall, the tested process-based model can be a useful tool for designing and pre-evaluation multiple combinations of crop management, species, and cultivars suitable for intercropping in diverse conditions.

@article{DEMIE2025109302,
title = {Evaluating a new intercrop model for capturing mixture effects with an extensive intercrop dataset},
journal = {Agriculture, Ecosystems & Environment},
volume = {378},
pages = {109302},
year = {2025},
issn = {0167-8809},
doi = {10.1016/j.agee.2024.109302},
url = {https://www.sciencedirect.com/science/article/pii/S0167880924004201},
author = {Dereje T. Demie and Daniel Wallach and Thomas F. Döring and Frank Ewert and Thomas Gaiser and Sofia Hadir and Gunther Krauss and Madhuri Paul and Ixchel M. Hernández-Ochoa and Rémi Vezy and Sabine J. Seidel},
keywords = {Cropping system, Cultivar choice, Diversification, Crop modeling, Crop mixtures},
abstract = {Cereal-legume intercrops have numerous advantages over monocultures. However, the intercrop’s performance depends on the plant genotypes, management, and environment. Process-based agro-ecosystem models are important tools to evaluate the performance of intercrop systems as field experiments are limited in the number of treatments. The objective of this study was to calibrate and evaluate a new process-based intercrop model using an extensive experimental data set and to test whether the model is suitable for comparing intercrop management strategies. The data set includes all combinations of 12 different spring wheat entries (SW, Triticum aestivum L.) with two faba bean (FB, Vicia faba L.) cultivars, at two sowing densities, in three different environments. The results show that the intercrop model was capable of simulating the absolute mixture (intercrop) effects (AME) for grain yield, above-ground biomass, and topsoil root biomass, for both crops. However, the intercrop model does not perform better than a benchmark that ignores the intercrop effects when simulating plant height, fraction of intercepted radiation, volumetric soil water content, and subsoil root biomass. The intercrop model predicted reasonably well the differences between species and between SW cultivars for grain yield and aboveground plant biomass. Overall, the tested process-based model can be a useful tool for designing and pre-evaluation multiple combinations of crop management, species, and cultivars suitable for intercropping in diverse conditions.}
}

2024

S. Roilo, T. R. Hofmeester, M. Frauendorf, A. Widén, and A. F. Cord, “The untapped potential of camera traps for farmland biodiversity monitoring: current practice and outstanding agroecological questions,” Remote Sensing in Ecology and Conservation, 2024. doi:10.1002/rse2.426
[BibTeX] [PDF]

@article{Roilo2024,
author = {Stephanie Roilo and Tim R. Hofmeester and Magali Frauendorf and Anna Widén and Anna F. Cord},
title = {The untapped potential of camera traps for farmland biodiversity monitoring: current practice and outstanding agroecological questions},
journal = {Remote Sensing in Ecology and Conservation},
year = {2024},
doi = {10.1002/rse2.426},
url = {https://zslpublications.onlinelibrary.wiley.com/doi/10.1002/rse2.426}
}

A. Vashisth, J. Rückin, F. Magistri, C. Stachniss, and M. Popović, “Deep Reinforcement Learning with Dynamic Graphs for Adaptive Informative Path Planning,” , vol. 9, iss. 9, pp. 7747-7754, 2024. doi:10.1109/LRA.2024.3421188
[BibTeX] [Code]

@article{vashisth2024ral,
author = {A. Vashisth and J. R\"uckin and F. Magistri and C.
Stachniss and M. Popovi\'c},
title = {{Deep Reinforcement Learning with Dynamic Graphs for Adaptive
Informative Path Planning}},
journal = ral,
volume = {9},
number = {9},
pages = {7747-7754},
year = 2024,
doi = {10.1109/LRA.2024.3421188},
codeurl = {https://github.com/dmar-bonn/ipp-rl-3d},
}

J. Rückin, F. Magistri, C. Stachniss, and M. Popović, “Active Learning of Robot Vision Using Adaptive Path Planning,” in Proc.~of the IROS Workshop on Label Efficient Learning Paradigms for Autonomoy at Scale , 2024.
[BibTeX] [PDF]

@inproceedings{rueckin2024irosws,
author = {J. R\"uckin and F. Magistri and C. Stachniss and M. Popovi\'c},
title = {{Active Learning of Robot Vision Using Adaptive Path Planning}},
booktitle = {Proc.~of the IROS Workshop on Label Efficient Learning Paradigms for Autonomoy at Scale},
year = 2024,
url = {https://arxiv.org/pdf/2410.10684},
}

E. A. Marks, J. Bömer, F. Magistri, A. Sah, J. Behley, and C. Stachniss, “BonnBeetClouds3D: A Dataset Towards Point Cloud-Based Organ-Level Phenotyping of Sugar Beet Plants Under Real Field Conditions.” 2024.
[BibTeX] [PDF]

@inproceedings{marks2024iros,
author = {E.A. Marks and J. B\"omer and F. Magistri and A. Sah and J. Behley and C. Stachniss},
title = {{BonnBeetClouds3D: A Dataset Towards Point Cloud-Based Organ-Level Phenotyping of Sugar Beet Plants Under Real Field Conditions}},
booktitle = iros,
year = 2024,
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/marks2024iros.pdf},
}

M. Zeller, D. Casado Herraez, B. Ayan, J. Behley, M. Heidingsfeld, and C. Stachniss, “SemRaFiner: Panoptic Segmentation in Sparse and Noisy Radar Point Clouds,” ral, 2024. doi:10.1109/LRA.2024.3502058
[BibTeX] [PDF]

@article{zeller2024ral,
author = {M. Zeller and Casado Herraez, D. and B. Ayan and J. Behley and M. Heidingsfeld and C. Stachniss},
title = {{SemRaFiner: Panoptic Segmentation in Sparse and Noisy Radar Point
Clouds}},
journal = ral,
year = {2024},
volume = {},
number = {},
pages = {},
issn = {2377-3766},
doi = {10.1109/LRA.2024.3502058},
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/zeller2024ral.pdf},
}

L. Lobefaro, M. V. R. Malladi, T. Guadagnino, and C. Stachniss, “Spatio-Temporal Consistent Mapping of Growing Plants for Agricultural Robots in the Wild,” in iros , 2024.
[BibTeX] [PDF] [Code] [Video]

@inproceedings{lobefaro2024iros,
author = {L. Lobefaro and M.V.R. Malladi and T. Guadagnino and C. Stachniss},
title = {{Spatio-Temporal Consistent Mapping of Growing Plants for Agricultural Robots in the Wild}},
booktitle = iros,
year = 2024,
codeurl = {https://github.com/PRBonn/spatio-temporal-mapping.git},
videourl = {https://youtu.be/bnWZWd5DHTg},
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/lobefaro2024iros.pdf},
}

L. Wiesmann, T. Läbe, L. Nunes, J. Behley, and C. Stachniss, “Joint Intrinsic and Extrinsic Calibration of Perception Systems Utilizing a Calibration Environment,” ral, vol. 9, iss. 10, pp. 9103-9110, 2024. doi:10.1109/LRA.2024.3457385
[BibTeX] [PDF]

@article{wiesmann2024ral,
author = {L. Wiesmann and T. L\"abe and L. Nunes and J. Behley and C. Stachniss},
title = {{Joint Intrinsic and Extrinsic Calibration of Perception Systems Utilizing a Calibration Environment}},
journal = ral,
year = {2024},
volume = {9},
number = {10},
pages = {9103-9110},
issn = {2377-3766},
doi = {10.1109/LRA.2024.3457385},
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/wiesmann2024ral.pdf},
}

M. Heep and E. Zell, “An Adaptive Screen-Space Meshing Approach for Normal Integration,” in European Conference on Computer Vision , 2024, p. 445–461. doi:10.1007/978-3-031-72920-1_25
[BibTeX] [Code] [Video]

@inproceedings{heep2024adaptive,
title={An Adaptive Screen-Space Meshing Approach for Normal Integration},
author={Heep, Moritz and Zell, Eduard},
booktitle={European Conference on Computer Vision},
pages={445--461},
year={2024},
doi={10.1007/978-3-031-72920-1_25},
codeurl={https://github.com/moritzheep/adaptive-screen-meshing},
videourl={https://www.youtube.com/watch?v=6m2SKqb1M5M},
}

L. V. Rasmussen, I. Grass, Z. Mehrabi, O. M. Smith, R. Bezner-Kerr, J. Blesh, L. A. Garibaldi, M. E. Isaac, C. M. Kennedy, H. Wittman, P. Batáry, D. Buchori, R. Cerda, J. Chará, D. W. Crowder, K. Darras, K. DeMaster, K. Garcia, M. Gómez, D. Gonthier, A. Guzman, P. Hidayat, J. Hipólito, M. Hirons, L. Hoey, D. James, I. John, A. D. Jones, D. S. Karp, Y. Kebede, C. B. Kerr, S. Klassen, M. Kotowska, H. Kreft, R. Llanque, C. Levers, D. J. Lizcano, A. Lu, S. Madsen, R. N. Marques, P. B. Martins, A. Melo, H. Nyantakyi-Frimpong, E. M. Olimpi, J. P. Owen, H. Pantevez, M. Qaim, S. Redlich, C. Scherber, A. R. Sciligo, S. Snapp, W. E. Snyder, I. Steffan-Dewenter, A. E. Stratton, J. M. Taylor, T. Tscharntke, V. Valencia, C. Vogel, and C. Kremen, “Joint Environmental and Social Benefits from Diversified Agriculture,” Science, vol. 384, p. 87–93, 2024. doi:10.1126/science.adj1914
[BibTeX]

@article{rasmussen2024diversified,
author = {Laura Vang Rasmussen and Ingo Grass and Zia Mehrabi and Olivia M. Smith and Rachel Bezner-Kerr and Jennifer Blesh and Lucas Alejandro Garibaldi and Marney E. Isaac and Christina M. Kennedy and Hannah Wittman and Péter Batáry and Damayanti Buchori and Rolando Cerda and Julián Chará and David W. Crowder and Kevin Darras and Kathryn DeMaster and Karina Garcia and Manuel Gómez and David Gonthier and Aidee Guzman and Purnama Hidayat and Juliana Hipólito and Mark Hirons and Lesli Hoey and Dana James and Innocensia John and Andrew D. Jones and Daniel S. Karp and Yodit Kebede and Carmen Bezner Kerr and Susanna Klassen and Martyna Kotowska and Holger Kreft and Ramiro Llanque and Christian Levers and Diego J. Lizcano and Adrian Lu and Sidney Madsen and Rosebelly Nunes Marques and Pedro Buss Martins and America Melo and Hanson Nyantakyi-Frimpong and Elissa M. Olimpi and Jeb P. Owen and Heiber Pantevez and Matin Qaim and Sarah Redlich and Christoph Scherber and Amber R. Sciligo and Sieglinde Snapp and William E. Snyder and Ingolf Steffan-Dewenter and Anne Elise Stratton and Joseph M. Taylor and Teja Tscharntke and Vivian Valencia and Cassandra Vogel and Claire Kremen},
title = {Joint Environmental and Social Benefits from Diversified Agriculture},
journal = {Science},
volume = {384},
pages = {87--93},
year = {2024},
doi = {10.1126/science.adj1914},
}

L. Chu, V. Shrestha, C. C. Schäfer, J. Niedens, G. W. Meyer, Z. Darnell, T. Kling, T. Dürr-Mayer, A. Abramov, M. Frey, H. Jessen, G. Schaaf, F. Hochholdinger, A. Nowak-Król, P. McSteen, R. Angelovici, and M. S. Matthes, “Association of the benzoxazinoid pathway with boron homeostasis in maize,” Plant Physiology, p. kiae611, 2024. doi:10.1093/plphys/kiae611
[BibTeX] [PDF]

Both deficiency and toxicity of the micronutrient boron lead to severe reductions in crop yield. Despite this agricultural importance, the molecular basis underlying boron homeostasis in plants remains unclear. To identify molecular players involved in boron homeostasis in maize (Zea mays L.), we measured boron levels in the Goodman-Buckler association panel and performed genome-wide association studies. These analyses identified a benzoxazinless (bx) gene, bx3, involved in the biosynthesis of benzoxazinoids, such as DIMBOA, which are major defense compounds in maize. Genes involved in DIMBOA biosynthesis are all located in close proximity in the genome, and benzoxazinoid biosynthesis mutants, including bx3, are all DIMBOA deficient. We determined that leaves of the bx3 mutant have a greater boron concentration than those of B73 control plants, which corresponded with enhanced leaf tip necrosis, a phenotype associated with boron toxicity. By contrast, other DIMBOA-deficient maize mutants did not show altered boron levels or the leaf tip necrosis phenotype, suggesting that boron is not associated with DIMBOA. Instead, our analyses suggest that the accumulation of boron is linked to the benzoxazinoid intermediates indolin-2-one (ION) and 3-hydroxy-ION. Therefore, our results connect boron homeostasis to the benzoxazinoid plant defense pathway through bx3 and specific intermediates, rendering the benzoxazinoid biosynthesis pathway a potential target for crop improvement under inadequate boron conditions.

@article{10.1093/plphys/kiae611,
author = {Chu, Liuyang and Shrestha, Vivek and Schäfer, Cay Christin and Niedens, Jan and Meyer, George W and Darnell, Zoe and Kling, Tyler and Dürr-Mayer, Tobias and Abramov, Aleksej and Frey, Monika and Jessen, Henning and Schaaf, Gabriel and Hochholdinger, Frank and Nowak-Król, Agnieszka and McSteen, Paula and Angelovici, Ruthie and Matthes, Michaela S},
title = {Association of the benzoxazinoid pathway with boron homeostasis in maize},
journal = {Plant Physiology},
pages = {kiae611},
year = {2024},
month = {11},
abstract = {Both deficiency and toxicity of the micronutrient boron lead to severe reductions in crop yield. Despite this agricultural importance, the molecular basis underlying boron homeostasis in plants remains unclear. To identify molecular players involved in boron homeostasis in maize (Zea mays L.), we measured boron levels in the Goodman-Buckler association panel and performed genome-wide association studies. These analyses identified a benzoxazinless (bx) gene, bx3, involved in the biosynthesis of benzoxazinoids, such as DIMBOA, which are major defense compounds in maize. Genes involved in DIMBOA biosynthesis are all located in close proximity in the genome, and benzoxazinoid biosynthesis mutants, including bx3, are all DIMBOA deficient. We determined that leaves of the bx3 mutant have a greater boron concentration than those of B73 control plants, which corresponded with enhanced leaf tip necrosis, a phenotype associated with boron toxicity. By contrast, other DIMBOA-deficient maize mutants did not show altered boron levels or the leaf tip necrosis phenotype, suggesting that boron is not associated with DIMBOA. Instead, our analyses suggest that the accumulation of boron is linked to the benzoxazinoid intermediates indolin-2-one (ION) and 3-hydroxy-ION. Therefore, our results connect boron homeostasis to the benzoxazinoid plant defense pathway through bx3 and specific intermediates, rendering the benzoxazinoid biosynthesis pathway a potential target for crop improvement under inadequate boron conditions.},
issn = {0032-0889},
doi = {10.1093/plphys/kiae611},
url = {https://www.phenorob.de/wp-content/uploads/2024/12/Chu_2024_Bx3.pdf},
}

Y. Ueda, K. Kondo, H. Saito, J. Pariasca-Tanaka, H. Takanashi, H. N. Ranaivo, M. Rakotondramanana, and M. Wissuwa, “Characterization of quantitative trait loci from DJ123 (aus) independently affecting panicle structure traits in indica rice cultivar IR64,” Molecular Breeding, vol. 44, iss. 9, p. 57, 2024. doi:10.1007/s11032-024-01494-5
[BibTeX]

@article{Ueda2024,
author = {Y. Ueda and K. Kondo and H. Saito and J. Pariasca-Tanaka and H. Takanashi and H. N. Ranaivo and M. Rakotondramanana and M. Wissuwa},
title = {Characterization of quantitative trait loci from DJ123 (aus) independently affecting panicle structure traits in indica rice cultivar IR64},
journal = {Molecular Breeding},
year = {2024},
volume = {44},
number = {9},
pages = {57},
month = {sep},
doi = {10.1007/s11032-024-01494-5}
}

C. Hubert-Schöler, S. Tsiaparas, K. Luhmer, M. D. Moll, M. Passon, M. Wüst, A. Schieber, and R. Pude, “Essential Oil Composition and Physiology of Three Mentha Genotypes Under Shaded Field Conditions,” Plants, vol. 13, iss. 22, 2024. doi:10.3390/plants13223155
[BibTeX] [PDF]

Mentha spp. are commonly used for the production of tea and for the extraction of essential oils (EOs). The key factor of mint quality is the content and composition of the EO. Health-promoting compounds such as menthol are desirable, whereas the presence of potentially health-damaging compounds such as menthofuran should be avoided. This study examines the effect of shading on the EO content and composition of three Mentha genotypes (Mentha × piperita ‘Multimentha’, Mentha × piperita ‘Fränkische Blaue’ and Mentha rotundifolia ‘Apfelminze’). The Mentha genotypes were cultivated in field trials for two years (2022–2023). Each genotype was shaded with a shading net (50% photosynthetic active radiation (PAR) reduction), and a control without shading was prepared. EO content was determined by steam distillation and EO composition was characterized by GC-MS analysis. Furthermore, biomass, vegetation indices (VIs) and the electron transport rate (ETR) were analyzed. While shading led to higher plant heights, higher EO content and a slightly reduced amount of undesired EO compounds, the unshaded control yielded a higher biomass accumulation. Significant genotypic differences were determined. In conclusion, the benefits of shading depend on the intended use and genotype selection.

@Article{plants13223155,
AUTHOR = {Hubert-Schöler, Charlotte and Tsiaparas, Saskia and Luhmer, Katharina and Moll, Marcel Dieter and Passon, Maike and Wüst, Matthias and Schieber, Andreas and Pude, Ralf},
TITLE = {Essential Oil Composition and Physiology of Three Mentha Genotypes Under Shaded Field Conditions},
JOURNAL = {Plants},
VOLUME = {13},
YEAR = {2024},
NUMBER = {22},
ARTICLE-NUMBER = {3155},
URL = {https://www.mdpi.com/2223-7747/13/22/3155},
ISSN = {2223-7747},
ABSTRACT = {Mentha spp. are commonly used for the production of tea and for the extraction of essential oils (EOs). The key factor of mint quality is the content and composition of the EO. Health-promoting compounds such as menthol are desirable, whereas the presence of potentially health-damaging compounds such as menthofuran should be avoided. This study examines the effect of shading on the EO content and composition of three Mentha genotypes (Mentha × piperita ‘Multimentha’, Mentha × piperita ‘Fränkische Blaue’ and Mentha rotundifolia ‘Apfelminze’). The Mentha genotypes were cultivated in field trials for two years (2022–2023). Each genotype was shaded with a shading net (50% photosynthetic active radiation (PAR) reduction), and a control without shading was prepared. EO content was determined by steam distillation and EO composition was characterized by GC-MS analysis. Furthermore, biomass, vegetation indices (VIs) and the electron transport rate (ETR) were analyzed. While shading led to higher plant heights, higher EO content and a slightly reduced amount of undesired EO compounds, the unshaded control yielded a higher biomass accumulation. Significant genotypic differences were determined. In conclusion, the benefits of shading depend on the intended use and genotype selection.},
DOI = {10.3390/plants13223155}
}

O. Ihalainen, T. Sandmann, U. Rascher, and M. Mõttus, “Illumination correction for close-range hyperspectral images using spectral invariants and random forest regression,” Remote Sensing of Environment, vol. 315, p. 114467, 2024. doi:10.1016/j.rse.2024.114467
[BibTeX] [PDF]

Identifying materials and retrieving their properties from spectral imagery is based on their spectral reflectance calculated from the ratio of reflected radiance to the incident irradiance. However, obtaining the true reflectances of materials within a vegetation canopy is challenging given the varying illumination conditions across the canopy – i.e., the irradiance incident on a surface inside the canopy – caused by its complex 3D structure. Instead, in remote sensing, reflectances are calculated from the ratio of the spectral radiance measured by the sensor to the top-of-canopy (TOC) spectral irradiance, resulting in apparent reflectances that can significantly differ from the true reflectance spectra. To address this issue, we present a physically based illumination correction method for retrieving the true reflectances from close-range hyperspectral TOC reflectance images. The method uses five spectral invariant parameters to predict the illumination conditions from TOC reflectance and compute the corrected spectrum using a physically based model. For computational efficiency, the spectrally invariant parameters were retrieved using random forest regression trained with Monte Carlo ray tracing simulations. The method was tested on close-range imaging spectroscopy data from dense and sparse vegetation canopies for which reference in situ spectral measurements were available. This work is a step toward resolving the 3D radiation regime in vegetation canopies from TOC hyperspectral imagery. The retrieved spectral invariants provide a physical connection to the structure of the observed vegetation canopy. The true spectra of artificial and natural materials in a vegetation canopy, determined under various illumination conditions, allow their more robust (bio)chemical characterization, opening new applications in vegetation monitoring and material detection, and machine learning makes it possible to apply the method rapidly to large hyperspectral image sets.

@article{IHALAINEN2024114467,
title = {Illumination correction for close-range hyperspectral images using spectral invariants and random forest regression},
journal = {Remote Sensing of Environment},
volume = {315},
pages = {114467},
year = {2024},
issn = {0034-4257},
doi = {10.1016/j.rse.2024.114467},
url = {https://www.sciencedirect.com/science/article/pii/S0034425724004930},
author = {Olli Ihalainen and Theresa Sandmann and Uwe Rascher and Matti Mõttus},
keywords = {Close-range, Hyperspectral, Imaging spectroscopy, Radiative transfer, p-theory, Spectral invariants, Monte Carlo ray tracing, Random forest, Inversion},
abstract = {Identifying materials and retrieving their properties from spectral imagery is based on their spectral reflectance calculated from the ratio of reflected radiance to the incident irradiance. However, obtaining the true reflectances of materials within a vegetation canopy is challenging given the varying illumination conditions across the canopy – i.e., the irradiance incident on a surface inside the canopy – caused by its complex 3D structure. Instead, in remote sensing, reflectances are calculated from the ratio of the spectral radiance measured by the sensor to the top-of-canopy (TOC) spectral irradiance, resulting in apparent reflectances that can significantly differ from the true reflectance spectra. To address this issue, we present a physically based illumination correction method for retrieving the true reflectances from close-range hyperspectral TOC reflectance images. The method uses five spectral invariant parameters to predict the illumination conditions from TOC reflectance and compute the corrected spectrum using a physically based model. For computational efficiency, the spectrally invariant parameters were retrieved using random forest regression trained with Monte Carlo ray tracing simulations. The method was tested on close-range imaging spectroscopy data from dense and sparse vegetation canopies for which reference in situ spectral measurements were available. This work is a step toward resolving the 3D radiation regime in vegetation canopies from TOC hyperspectral imagery. The retrieved spectral invariants provide a physical connection to the structure of the observed vegetation canopy. The true spectra of artificial and natural materials in a vegetation canopy, determined under various illumination conditions, allow their more robust (bio)chemical characterization, opening new applications in vegetation monitoring and material detection, and machine learning makes it possible to apply the method rapidly to large hyperspectral image sets.}
}

J. Baumert, T. Heckelei, and H. Storm, “Probabilistic crop type mapping for ex-ante modelling and spatial disaggregation,” Ecological Informatics, vol. 83, p. 102836, 2024. doi:10.1016/j.ecoinf.2024.102836
[BibTeX] [PDF]

Agricultural land use and management fundamentally impacts the condition of natural resources like waterbodies, soils, and biodiversity. Modelling the anthropogenic effects on those resources over time requires detailed knowledge of the temporal and spatial distribution of crops. However, currently available crop type maps for Europe either lack the required spatial resolution or the temporal and spatial coverage. We develop and apply a probabilistic, spatially explicit crop type mapping approach that is suitable for ex-post and ex-ante modelling. The approach allows to quantify epistemic and aleatoric uncertainty related to estimated crop shares by providing an ensemble of maps. We implement the method for the EU-28 for the years 2010 – 2020, distinguishing between 28 different crop types at 1 km resolution. Based on a model of the data generating process that conceptually links field-, grid cell- and region-level crop acreages, our approach considers soil, climate, and topography information, as well as administrative data. The validation with ground-truthing data for France indicates that the generated crop type maps are plausible. The provided uncertainty intervals capture differences in uncertainty across space and time and correctly identify grid cells and crops where estimations are less precise. The generated maps constitute a unique data product of high practical value, e.g., for agri-environmental modelling applications. We see additional potential in using the approach to disaggregate the regional or national predictions of socio-economic ex-ante prediction models.

@article{BAUMERT2024102836,
title = {Probabilistic crop type mapping for ex-ante modelling and spatial disaggregation},
journal = {Ecological Informatics},
volume = {83},
pages = {102836},
year = {2024},
issn = {1574-9541},
doi = {10.1016/j.ecoinf.2024.102836},
url = {https://www.sciencedirect.com/science/article/pii/S1574954124003789},
author = {Josef Baumert and Thomas Heckelei and Hugo Storm},
keywords = {Probabilistic crop mapping, Crop choice modelling, Spatial disaggregation, Uncertainty quantification, Information fusion},
abstract = {Agricultural land use and management fundamentally impacts the condition of natural resources like waterbodies, soils, and biodiversity. Modelling the anthropogenic effects on those resources over time requires detailed knowledge of the temporal and spatial distribution of crops. However, currently available crop type maps for Europe either lack the required spatial resolution or the temporal and spatial coverage. We develop and apply a probabilistic, spatially explicit crop type mapping approach that is suitable for ex-post and ex-ante modelling. The approach allows to quantify epistemic and aleatoric uncertainty related to estimated crop shares by providing an ensemble of maps. We implement the method for the EU-28 for the years 2010 – 2020, distinguishing between 28 different crop types at 1 km resolution. Based on a model of the data generating process that conceptually links field-, grid cell- and region-level crop acreages, our approach considers soil, climate, and topography information, as well as administrative data. The validation with ground-truthing data for France indicates that the generated crop type maps are plausible. The provided uncertainty intervals capture differences in uncertainty across space and time and correctly identify grid cells and crops where estimations are less precise. The generated maps constitute a unique data product of high practical value, e.g., for agri-environmental modelling applications. We see additional potential in using the approach to disaggregate the regional or national predictions of socio-economic ex-ante prediction models.}
}

J. Weyler, F. Magistri, E. Marks, Y. L. Chong, M. Sodano, G. Roggiolani, N. Chebrolu, C. Stachniss, and J. Behley, “PhenoBench: A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domain,” , 2024. doi:10.1109/TPAMI.2024.3419548
[BibTeX] [PDF] [Code]

@article{weyler2024tpami,
author = {J. Weyler and F. Magistri and E. Marks and Y.L. Chong and M. Sodano and G. Roggiolani and N. Chebrolu and C. Stachniss and J. Behley},
title = {{PhenoBench: A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domain}},
journal = tpami,
year = {2024},
volume = {},
number = {},
pages = {},
doi = {10.1109/TPAMI.2024.3419548},
codeurl = {https://github.com/PRBonn/phenobench},
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/weyler2023arxiv.pdf},
}

F. Esser, G. Tombrink, A. Cornelißen, L. Klingbeil, and H. Kuhlmann, “System Calibration of a Field Phenotyping Robot with Multiple High-Precision Profile Laser Scanners,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) , 2024, pp. 8471-8477. doi:10.1109/ICRA57147.2024.10610208
[BibTeX]

@INPROCEEDINGS{10610208,
author={Esser, Felix and Tombrink, Gereon and Cornelißen, André and Klingbeil, Lasse and Kuhlmann, Heiner},
booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)},
title={System Calibration of a Field Phenotyping Robot with Multiple High-Precision Profile Laser Scanners},
year={2024},
volume={},
number={},
pages={8471-8477},
keywords={Point cloud compression;Accuracy;Three-dimensional displays;Systematics;Service robots;Lasers;Measurement by laser beam},
doi={10.1109/ICRA57147.2024.10610208}}

J. Yi, Y. Luo, M. Deichmann, G. Schaaf, and J. Gall, “MV-Match: Multi-View Matching for Domain-Adaptive Identification of Plant Nutrient Deficiencies,” in British Machine Vision Conference (BMVC’24) , 2024.
[BibTeX] [PDF]

@inproceedings{Yi2024,
author = {Yi, J. and Luo, Y. and Deichmann, M. and Schaaf, G. and Gall, J.},
title = {MV-Match: Multi-View Matching for Domain-Adaptive Identification of Plant Nutrient Deficiencies},
booktitle = {British Machine Vision Conference (BMVC'24)},
year = {2024},
url = {https://pages.iai.uni-bonn.de/gall_juergen/download/jgall_multiview_domain_nutrient_bmvc24.pdf},
note = {https://pages.iai.uni-bonn.de/gall_juergen/download/jgall_multiview_domain_nutrient_bmvc24_supp.pdf},
}

Y. Wu, T. Guadagnino, L. Wiesmann, L. Klingbeil, C. Stachniss, and H. Kuhlmann, “LIO-EKF: High Frequency LiDAR-Inertial Odometry using Extended Kalman Filters.” 2024.
[BibTeX] [PDF] [Code] [Video]

@inproceedings{wu2024icra,
author = {Y. Wu and T. Guadagnino and L. Wiesmann and L. Klingbeil and C. Stachniss and H. Kuhlmann},
title = {{LIO-EKF: High Frequency LiDAR-Inertial Odometry using Extended Kalman Filters}},
booktitle = icra,
year = 2024,
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/wu2024icra.pdf},
codeurl = {https://github.com/YibinWu/LIO-EKF},
videourl = {https://youtu.be/MoJTqEYl1ME},
}

A. Dreier, B. Jost, H. Kuhlmann, and L. Klingbeil, ,” Journal of Applied Geodesy, vol. 18, iss. 1, p. 97–113, 2024. doi:10.1515/jag-2022-0029
[BibTeX] [PDF]

@article{DreierJostKuhlmannKlingbeil+2024+97+113,
url = {https://doi.org/10.1515/jag-2022-0029},
title = {Investigations of the scan characteristics with special focus on multi-target capability for the 2D laser scanner RIEGL miniVUX-2UAV},
title = {},
author = {Ansgar Dreier and Berit Jost and Heiner Kuhlmann and Lasse Klingbeil},
pages = {97--113},
volume = {18},
number = {1},
journal = {Journal of Applied Geodesy},
doi = {10.1515/jag-2022-0029},
year = {2024},
}

D. Schulz, C. Stetter, J. Muro, J. Spekker, J. Börner, A. F. Cord, and R. Finger, “Trade-offs between grassland plant biodiversity and yields are heterogenous across Germany,” Communications Earth & Environment, vol. 5, iss. 1, p. 514, 2024. doi:10.1038/s43247-024-01685-0
[BibTeX] [PDF]

@article{Schulz2024,
author = {Dario Schulz and Christian Stetter and Javier Muro and Jonas Spekker and Jan Börner and Anna F. Cord and Robert Finger},
title = {Trade-offs between grassland plant biodiversity and yields are heterogenous across Germany},
journal = {Communications Earth \& Environment},
year = {2024},
volume = {5},
number = {1},
pages = {514},
doi = {10.1038/s43247-024-01685-0},
url = {https://doi.org/10.1038/s43247-024-01685-0},
issn = {2662-4435}
}

B. Yu, C. Zhou, Z. Wang, M. Bucher, G. Schaaf, R. J. H. Sawers, X. Chen, F. Hochholdinger, C. Zou, and P. Yu, “Maize zinc uptake is influenced by arbuscular mycorrhizal symbiosis under various soil phosphorus availabilities,” New Phytologist, vol. 243, iss. 5, pp. 1936-1950, 2024. doi:10.1111/nph.19952
[BibTeX] [PDF]

Summary The antagonistic interplay between phosphorus (P) and zinc (Zn) in plants is well established. However, the molecular mechanisms mediating those interactions as influenced by arbuscular mycorrhizal (AM) symbiosis remain unclear. We investigated Zn concentrations, root AM symbiosis, and transcriptome profiles of maize roots grown under field conditions upon different P levels. We also validated genotype-dependent P–Zn uptake in selected genotypes from a MAGIC population and conducted mycorrhizal inoculation experiments using mycorrhizal-defective mutant pht1;6 to elucidate the significance of AM symbiosis in P–Zn antagonism. Finally, we assessed how P supply affects Zn transporters and Zn uptake in extraradical hyphae within a three-compartment system. Elevated P levels led to a significant reduction in maize Zn concentration across the population, correlating with a marked decline in AM symbiosis, thus elucidating the P–Zn antagonism. We also identified ZmPht1;6 is crucial for AM symbiosis and confirmed that P–Zn antagonistic uptake is dependent on AM symbiosis. Moreover, we found that high P suppressed the expression of the fungal RiZRT1 and RiZnT1 genes, potentially impacting hyphal Zn uptake. We conclude that high P exerts systemic regulation over root and AM hyphae-mediated Zn uptake in maize. These findings hold implications for breeding Zn deficiency-tolerant maize varieties.

@article{https://doi.org/10.1111/nph.19952,
author = {Yu, Baogang and Zhou, Chengxiang and Wang, Zhonghua and Bucher, Marcel and Schaaf, Gabriel and Sawers, Ruairidh J. H. and Chen, Xinping and Hochholdinger, Frank and Zou, Chunqin and Yu, Peng},
title = {Maize zinc uptake is influenced by arbuscular mycorrhizal symbiosis under various soil phosphorus availabilities},
journal = {New Phytologist},
volume = {243},
number = {5},
pages = {1936-1950},
keywords = {arbuscular mycorrhizal, extraradical hyphae, maize, phosphorus, RNA sequencing, zinc, zinc transporter},
doi = {10.1111/nph.19952},
url = {https://nph.onlinelibrary.wiley.com/doi/abs/10.1111/nph.19952},
eprint = {https://nph.onlinelibrary.wiley.com/doi/pdf/10.1111/nph.19952},
abstract = {Summary The antagonistic interplay between phosphorus (P) and zinc (Zn) in plants is well established. However, the molecular mechanisms mediating those interactions as influenced by arbuscular mycorrhizal (AM) symbiosis remain unclear. We investigated Zn concentrations, root AM symbiosis, and transcriptome profiles of maize roots grown under field conditions upon different P levels. We also validated genotype-dependent P–Zn uptake in selected genotypes from a MAGIC population and conducted mycorrhizal inoculation experiments using mycorrhizal-defective mutant pht1;6 to elucidate the significance of AM symbiosis in P–Zn antagonism. Finally, we assessed how P supply affects Zn transporters and Zn uptake in extraradical hyphae within a three-compartment system. Elevated P levels led to a significant reduction in maize Zn concentration across the population, correlating with a marked decline in AM symbiosis, thus elucidating the P–Zn antagonism. We also identified ZmPht1;6 is crucial for AM symbiosis and confirmed that P–Zn antagonistic uptake is dependent on AM symbiosis. Moreover, we found that high P suppressed the expression of the fungal RiZRT1 and RiZnT1 genes, potentially impacting hyphal Zn uptake. We conclude that high P exerts systemic regulation over root and AM hyphae-mediated Zn uptake in maize. These findings hold implications for breeding Zn deficiency-tolerant maize varieties.},
year = {2024}
}

L. Zhang, S. Poll, and S. Kollet, “Assessing the Performance of Flux Imbalance Prediction Models Using Large Eddy Simulations Over Heterogeneous Land Surfaces,” Boundary-Layer Meteorology, vol. 190, iss. 10, p. 43, 2024. doi:10.1007/s10546-024-00880-y
[BibTeX] [PDF]

Accurate representation of heat fluxes is crucial for understanding land–atmosphere interactions and improving atmospheric simulations. However, a common issue arises with flux imbalance, where the measured turbulent heat flux tends to be underestimated due to the nonlocal effects of atmospheric secondary circulations. This study evaluated four flux imbalance prediction models by analyzing data from large eddy simulations performed over heterogeneous land surfaces. For that, a checkerboard pattern of soil moisture was used to define the lower boundary conditions for the atmosphere, across heterogeneity scales ranging from 50 m to 2.4 km. The results show that the selected models can effectively predict flux imbalance when provided with proper semi-empirical factors. The presence of two distinct secondary circulations, thermally-induced mesoscale circulation and turbulent organized structures, account for the nonlinear effect of the heterogeneity scale on the flux imbalance, but it does not affect the performance of the selected models. This study suggests that the flux imbalance prediction models are useful for improving e.g. eddy-covariance measurements. Additionally, a quadrant analysis showed an increasing difference between ejections and sweeps with height, which explains the decrease and increase of the turbulent heat flux and flux imbalance, respectively, and underscores the importance of accounting for vertical variations in turbulent fluxes to represent atmospheric processes accurately.

@article{Zhang2024,
author = {Lijie Zhang and Stefan Poll and Stefan Kollet},
title = {Assessing the Performance of Flux Imbalance Prediction Models Using Large Eddy Simulations Over Heterogeneous Land Surfaces},
journal = {Boundary-Layer Meteorology},
year = {2024},
volume = {190},
number = {10},
pages = {43},
doi = {10.1007/s10546-024-00880-y},
url = {https://doi.org/10.1007/s10546-024-00880-y},
issn = {1573-1472},
abstract = {Accurate representation of heat fluxes is crucial for understanding land–atmosphere interactions and improving atmospheric simulations. However, a common issue arises with flux imbalance, where the measured turbulent heat flux tends to be underestimated due to the nonlocal effects of atmospheric secondary circulations. This study evaluated four flux imbalance prediction models by analyzing data from large eddy simulations performed over heterogeneous land surfaces. For that, a checkerboard pattern of soil moisture was used to define the lower boundary conditions for the atmosphere, across heterogeneity scales ranging from 50 m to 2.4 km. The results show that the selected models can effectively predict flux imbalance when provided with proper semi-empirical factors. The presence of two distinct secondary circulations, thermally-induced mesoscale circulation and turbulent organized structures, account for the nonlinear effect of the heterogeneity scale on the flux imbalance, but it does not affect the performance of the selected models. This study suggests that the flux imbalance prediction models are useful for improving e.g. eddy-covariance measurements. Additionally, a quadrant analysis showed an increasing difference between ejections and sweeps with height, which explains the decrease and increase of the turbulent heat flux and flux imbalance, respectively, and underscores the importance of accounting for vertical variations in turbulent fluxes to represent atmospheric processes accurately.}
}

A. Haupenthal, P. Duddek, P. Benard, M. Knott, A. Carminati, H. F. Jungkunst, E. Kroener, and N. Brüggemann, “A root mucilage analogue from chia seeds reduces soil gas diffusivity,” European Journal of Soil Science, vol. 75, iss. 5, p. e13576, 2024. doi:10.1111/ejss.13576
[BibTeX] [PDF]

Abstract Gas exchange in the soil is determined by the size and connectivity of air-filled pores. Root mucilage reduces air-filled pore connectivity and thus gas diffusivity. It is unclear to what extent mucilage affects soil pore connectivity and tortuosity. The aim of this study was to gain a better understanding of gas diffusion processes in the rhizosphere by explaining the geometric alterations of the soil pore space induced by mucilage. We quantified the effect of a root mucilage analogue collected from chia seeds without intrinsic respiratory activity on oxygen diffusion at different water contents during drying–rewetting cycles in a diffusion chamber experiment. Quantification of oxygen diffusion showed that mucilage decreased the gas diffusion coefficient in dry soil without affecting air-filled porosity. Without mucilage, a hysteresis in gas diffusion coefficient during a drying–rewetting cycle was observed. The effect depended on particle size and diminished with increasing mucilage content. X-ray computed tomography imaging indicated a hysteresis in the connectivity of the gas phase during a drying–rewetting cycle for samples without mucilage. This effect was attenuated with increasing mucilage content. Furthermore, electron microscopy showed that mucilage structures formed in drying soil increase with mucilage content, thereby progressively reducing the connectivity of the gas phase. In conclusion, the effect of mucilage on soil gas diffusion highly depends on soil texture and mucilage content. The diminishing hysteresis with the addition of mucilage suggests that plant roots secrete mucilage to balance oxygen availability and water content, even under fluctuating moisture conditions.

@article{https://doi.org/10.1111/ejss.13576,
author = {Haupenthal, Adrian and Duddek, Patrick and Benard, Pascal and Knott, Mathilde and Carminati, Andrea and Jungkunst, Hermann F. and Kroener, Eva and Brüggemann, Nicolas},
title = {A root mucilage analogue from chia seeds reduces soil gas diffusivity},
journal = {European Journal of Soil Science},
volume = {75},
number = {5},
pages = {e13576},
keywords = {gas diffusion coefficient, hysteresis, liquid bridges, pore connectivity, rhizosphere, root exudates, root respiration, root–soil interactions, X-ray CT},
doi = {10.1111/ejss.13576},
url = {https://bsssjournals.onlinelibrary.wiley.com/doi/abs/10.1111/ejss.13576},
eprint = {https://bsssjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/ejss.13576},
abstract = {Abstract Gas exchange in the soil is determined by the size and connectivity of air-filled pores. Root mucilage reduces air-filled pore connectivity and thus gas diffusivity. It is unclear to what extent mucilage affects soil pore connectivity and tortuosity. The aim of this study was to gain a better understanding of gas diffusion processes in the rhizosphere by explaining the geometric alterations of the soil pore space induced by mucilage. We quantified the effect of a root mucilage analogue collected from chia seeds without intrinsic respiratory activity on oxygen diffusion at different water contents during drying–rewetting cycles in a diffusion chamber experiment. Quantification of oxygen diffusion showed that mucilage decreased the gas diffusion coefficient in dry soil without affecting air-filled porosity. Without mucilage, a hysteresis in gas diffusion coefficient during a drying–rewetting cycle was observed. The effect depended on particle size and diminished with increasing mucilage content. X-ray computed tomography imaging indicated a hysteresis in the connectivity of the gas phase during a drying–rewetting cycle for samples without mucilage. This effect was attenuated with increasing mucilage content. Furthermore, electron microscopy showed that mucilage structures formed in drying soil increase with mucilage content, thereby progressively reducing the connectivity of the gas phase. In conclusion, the effect of mucilage on soil gas diffusion highly depends on soil texture and mucilage content. The diminishing hysteresis with the addition of mucilage suggests that plant roots secrete mucilage to balance oxygen availability and water content, even under fluctuating moisture conditions.},
year = {2024}
}

Y. Pan, X. Zhong, L. Wiesmann, T. Posewsky, J. Behley, and C. Stachniss, “PIN-SLAM: LiDAR SLAM Using a Point-Based Implicit Neural Representation for Achieving Global Map Consistency,” , vol. 40, pp. 4045-4064, 2024. doi:10.1109/TRO.2024.3422055
[BibTeX] [PDF] [Code]

@article{pan2024tro,
author = {Y. Pan and X. Zhong and L. Wiesmann and T. Posewsky and J. Behley and C. Stachniss},
title = {{PIN-SLAM: LiDAR SLAM Using a Point-Based Implicit Neural Representation for Achieving Global Map Consistency}},
journal = tro,
year = {2024},
pages = {4045-4064},
volume = {40},
doi = {10.1109/TRO.2024.3422055},
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/pan2024tro.pdf},
codeurl = {https://github.com/PRBonn/PIN_SLAM},
}

L. Wiesmann, T. Läbe, L. Nunes, J. Behley, and C. Stachniss, “Joint Intrinsic and Extrinsic Calibration of Perception Systems Utilizing a Calibration Environment,” IEEE Robotics and Automation Letters, vol. 9, iss. 10, pp. 9103-9110, 2024. doi:10.1109/LRA.2024.3457385
[BibTeX] [PDF]

@ARTICLE{10670288,
author={Wiesmann, Louis and Läbe, Thomas and Nunes, Lucas and Behley, Jens and Stachniss, Cyrill},
journal={IEEE Robotics and Automation Letters},
title={Joint Intrinsic and Extrinsic Calibration of Perception Systems Utilizing a Calibration Environment},
year={2024},
volume={9},
number={10},
pages={9103-9110},
keywords={Sensors;Calibration;Cameras;Sensor systems;Laser radar;Robot vision systems;Robot sensing systems;Calibration and identification;mapping;sensor fusion},
doi={10.1109/LRA.2024.3457385},
url={https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/wiesmann2024ral.pdf}}

X. He, D. Wang, Y. Jiang, M. Li, M. Delgado-Baquerizo, C. McLaughlin, C. Marcon, L. Guo, M. Baer, Y. A. T. Moya, N. von Wirén, M. Deichmann, G. Schaaf, H. Piepho, Z. Yang, J. Yang, B. Yim, K. Smalla, S. Goormachtig, F. T. de Vries, H. Hüging, M. Baer, R. J. H. Sawers, J. C. Reif, F. Hochholdinger, X. Chen, and P. Yu, “Heritable microbiome variation is correlated with source environment in locally adapted maize varieties,” Nature Plants, vol. 10, iss. 4, p. 598–617, 2024. doi:10.1038/s41477-024-01654-7
[BibTeX] [PDF]

@article{He2024,
author = {Xiaoming He and Danning Wang and Yong Jiang and Meng Li and Manuel Delgado-Baquerizo and Chloee McLaughlin and Caroline Marcon and Li Guo and Marcel Baer and Yudelsy A. T. Moya and Nicolaus von Wirén and Marion Deichmann and Gabriel Schaaf and Hans-Peter Piepho and Zhikai Yang and Jinliang Yang and Bunlong Yim and Kornelia Smalla and Sofie Goormachtig and Franciska T. de Vries and Hubert Hüging and Mareike Baer and Ruairidh J. H. Sawers and Jochen C. Reif and Frank Hochholdinger and Xinping Chen and Peng Yu},
title = {Heritable microbiome variation is correlated with source environment in locally adapted maize varieties},
journal = {Nature Plants},
volume = {10},
number = {4},
pages = {598--617},
year = {2024},
doi = {10.1038/s41477-024-01654-7},
url = {https://doi.org/10.1038/s41477-024-01654-7},
issn = {2055-0278}
}

D. Wüpper, W. A. Oluoch, and Hadi, “Satellite Data in Agricultural and Environmental Economics: Theory and Practice,” International Association of Agricultural Economists (IAAE), IAAE 2024 Conference, August 2-7, 2024, New Delhi, India 344359, 2024. doi:10.22004/ag.econ.344359
[BibTeX] [PDF]

Agricultural and environmental economists are in the fortunate position that a lot of what is happening on the ground is observable from space. Most agricultural production happens in the open and one can see from space when and where innovations are adopted, crop yields change, or forests are converted to pastures, to name just a few examples. However, converting images into measurements of a particular variable is not trivial, as there are more pitfalls and nuances than “meet the eye”. Overall, however, research benefits tremendously from advances in available satellite data as well as complementary tools, such as cloud-based platforms for data processing, and machine learning algorithms to detect phenomena and mapping variables. The focus of this keynote is to provide agricultural and environmental economists with an accessible introduction to working with satellite data, show-case applications, discuss advantages and weaknesses of satellite data, and emphasize best practices. This is supported by extensive Supplementary Materials, explaining the technical foundations, describing in detail how to create different variables, sketch out work flows, and a discussion of required resources and skills. Last but not least, example data and reproducible codes are available online.

@TECHREPORT{RePEc:ags:cfcp15:344359,
title = {Satellite Data in Agricultural and Environmental Economics: Theory and Practice},
author = {Wüpper, David and Oluoch, Wyclife Agumba and Hadi},
year = {2024},
institution = {International Association of Agricultural Economists (IAAE)},
type = {IAAE 2024 Conference, August 2-7, 2024, New Delhi, India},
number = {344359},
abstract = {Agricultural and environmental economists are in the fortunate position that a lot of what is happening on the ground is observable from space. Most agricultural production happens in the open and one can see from space when and where innovations are adopted, crop yields change, or forests are converted to pastures, to name just a few examples. However, converting images into measurements of a particular variable is not trivial, as there are more pitfalls and nuances than “meet the eye”. Overall, however, research benefits tremendously from advances in available satellite data as well as complementary tools, such as cloud-based platforms for data processing, and machine learning algorithms to detect phenomena and mapping variables. The focus of this keynote is to provide agricultural and environmental economists with an accessible introduction to working with satellite data, show-case applications, discuss advantages and weaknesses of satellite data, and emphasize best practices. This is supported by extensive Supplementary Materials, explaining the technical foundations, describing in detail how to create different variables, sketch out work flows, and a discussion of required resources and skills. Last but not least, example data and reproducible codes are available online.},
keywords = {Environmental Economics and Policy; Research Methods/ Statistical Methods},
doi = {10.22004/ag.econ.344359},
url = {https://EconPapers.repec.org/RePEc:ags:cfcp15:344359}
}

@article{Schulz2024,
author = {Dario Schulz and Christian Stetter and Javier Muro and Jonas Spekker and Jan Börner and Anna F. Cord and Robert Finger},
title = {Trade-offs between grassland plant biodiversity and yields are heterogenous across Germany},
journal = {Communications Earth \& Environment},
year = {2024},
volume = {5},
number = {1},
pages = {514},
doi = {10.1038/s43247-024-01685-0},
url = {https://doi.org/10.1038/s43247-024-01685-0},
issn = {2662-4435}
}

T. H. Nguyen, G. Lopez, S. J. Seidel, L. Lärm, F. M. Bauer, A. Klotzsche, A. Schnepf, T. Gaiser, H. Hüging, and F. Ewert, “Multi-year aboveground data of minirhizotron facilities in Selhausen,” Scientific Data, vol. 11, iss. 1, p. 674, 2024. doi:10.1038/s41597-024-03535-2
[BibTeX] [PDF]

Abstract Improved understanding of crops’ response to soil water stress is important to advance soil-plant system models and to support crop breeding, crop and varietal selection, and management decisions to minimize negative impacts. Studies on eco-physiological crop characteristics from leaf to canopy for different soil water conditions and crops are often carried out at controlled conditions. In-field measurements under realistic field conditions and data of plant water potential, its links with CO 2 and H 2 O gas fluxes, and crop growth processes are rare. Here, we presented a comprehensive data set collected from leaf to canopy using sophisticated and comprehensive sensing techniques (leaf chlorophyll, stomatal conductance and photosynthesis, canopy CO 2 exchange, sap flow, and canopy temperature) including detailed crop growth characteristics based on destructive methods (crop height, leaf area index, aboveground biomass, and yield). Data were acquired under field conditions with contrasting soil types, water treatments, and different cultivars of wheat and maize. The data from 2016 up to now will be made available for studying soil/water-plant relations and improving soil-plant-atmospheric continuum models.

@article{nguyen_multi-year_2024,
title = {Multi-year aboveground data of minirhizotron facilities in {Selhausen}},
volume = {11},
issn = {2052-4463},
url = {https://www.nature.com/articles/s41597-024-03535-2},
doi = {10.1038/s41597-024-03535-2},
abstract = {Abstract
Improved understanding of crops’ response to soil water stress is important to advance soil-plant system models and to support crop breeding, crop and varietal selection, and management decisions to minimize negative impacts. Studies on eco-physiological crop characteristics from leaf to canopy for different soil water conditions and crops are often carried out at controlled conditions. In-field measurements under realistic field conditions and data of plant water potential, its links with CO
2
and H
2
O gas fluxes, and crop growth processes are rare. Here, we presented a comprehensive data set collected from leaf to canopy using sophisticated and comprehensive sensing techniques (leaf chlorophyll, stomatal conductance and photosynthesis, canopy CO
2
exchange, sap flow, and canopy temperature) including detailed crop growth characteristics based on destructive methods (crop height, leaf area index, aboveground biomass, and yield). Data were acquired under field conditions with contrasting soil types, water treatments, and different cultivars of wheat and maize. The data from 2016 up to now will be made available for studying soil/water-plant relations and improving soil-plant-atmospheric continuum models.},
language = {en},
number = {1},
urldate = {2024-08-20},
journal = {Scientific Data},
author = {Nguyen, Thuy Huu and Lopez, Gina and Seidel, Sabine J. and Lärm, Lena and Bauer, Felix Maximilian and Klotzsche, Anja and Schnepf, Andrea and Gaiser, Thomas and Hüging, Hubert and Ewert, Frank},
month = jun,
year = {2024},
pages = {674},
}

F. Noack, D. Engist, J. Gantois, V. Gaur, B. F. Hyjazie, A. Larsen, L. K. M’Gonigle, A. Missirian, M. Qaim, R. D. Sargent, E. Souza-Rodrigues, and C. Kremen, “Environmental impacts of genetically modified crops,” Science, vol. 385, iss. 6712, p. eado9340, 2024. doi:10.1126/science.ado9340
[BibTeX]

@article{article,
author = {Frederik Noack and Dennis Engist and Josephine Gantois and Vasundhara Gaur and Batoule F. Hyjazie and Ashley Larsen and Leithen K. M’Gonigle and Anouch Missirian and Matin Qaim and Risa D. Sargent and Eduardo Souza-Rodrigues and Claire Kremen },
title = {Environmental impacts of genetically modified crops},
journal = {Science},
volume = {385},
number = {6712},
pages = {eado9340},
year = {2024},
doi = {10.1126/science.ado9340},
}

V. Michels, C. Chou, M. Weigand, Y. Wu, and A. Kemna, “Quantitative phenotyping of crop roots with spectral electrical impedance tomography: a rhizotron study with optimized measurement design,” Plant Methods, vol. 20, 2024. doi:10.1186/s13007-024-01247-7
[BibTeX] [PDF]

@article{article,
author = {Michels, Valentin and Chou, Chunwei and Weigand, Maximilian and Wu, Yuxin and Kemna, Andreas},
year = {2024},
month = {08},
pages = {},
title = {Quantitative phenotyping of crop roots with spectral electrical impedance tomography: a rhizotron study with optimized measurement design},
volume = {20},
journal = {Plant Methods},
doi = {10.1186/s13007-024-01247-7},
url = {https://plantmethods.biomedcentral.com/articles/10.1186/s13007-024-01247-7},
}

S. Hadir, T. Döring, E. Justes, D. Demie, M. Paul, N. Legner, R. Kemper, T. Gaiser, O. Weedon, F. Ewert, and S. Seidel, “Root growth and belowground interactions in spring wheat /faba bean intercrops,” Plant and Soil, pp. 1-20, 2024. doi:10.1007/s11104-024-06742-3
[BibTeX] [PDF]

@article{article,
author = {Hadir, Sofia and Döring, Thomas and Justes, Eric and Demie, Dereje and Paul, Madhuri and Legner, Nicole and Kemper, Roman and Gaiser, Thomas and Weedon, Odette and Ewert, Frank and Seidel, Sabine},
year = {2024},
month = {05},
pages = {1-20},
title = {Root growth and belowground interactions in spring wheat /faba bean intercrops},
journal = {Plant and Soil},
doi = {10.1007/s11104-024-06742-3},
url = {https://link.springer.com/article/10.1007/s11104-024-06742-3#Fun},
}

D. Wang, X. He, M. Baer, K. Lami, B. Yu, A. Tassinari, S. Salvi, G. Schaaf, F. Hochholdinger, and P. Yu, “Lateral root enriched Massilia associated with plant flowering in maize,” Microbiome, vol. 12, 2024. doi:10.1186/s40168-024-01839-4
[BibTeX] [PDF]

@article{article,
author = {Wang, Danning and He, Xiaoming and Baer, Marcel and Lami, Klea and Yu, Baogang and Tassinari, Alberto and Salvi, Silvio and Schaaf, Gabriel and Hochholdinger, Frank and Yu, Peng},
year = {2024},
month = {07},
pages = {},
title = {Lateral root enriched Massilia associated with plant flowering in maize},
volume = {12},
journal = {Microbiome},
doi = {10.1186/s40168-024-01839-4},
url = {https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-024-01839-4#Abs1},
}

E. Chakhvashvili, M. Machwitz, M. Antala, O. Rozenstein, E. Prikaziuk, M. Schlerf, P. Näthe, Q. Wan, J. Komarek, T. Klouek, S. Wieneke, B. Siegmann, S. Kefauver, M. Kycko, H. Balde, V. Paz, J. A. J. Berni, H. Buddenbaum, and L. Hänchen, “Crop stress detection from UAVs: best practices and lessons learned for exploiting sensor synergies,” Precision Agriculture, 2024. doi:10.1007/s11119-024-10168-3
[BibTeX] [PDF]

@article{article,
author = {Chakhvashvili, Erekle and Machwitz, Miriam and Antala, Michal and Rozenstein, Offer and Prikaziuk, Egor and Schlerf, Martin and Näthe, Paul and Wan, Quanxing and Komarek, Jan and Klouek, Tomáš and Wieneke, Sebastian and Siegmann, Bastian and Kefauver, Shawn and Kycko, Marlena and Balde, Hamadou and Paz, Veronica and J. Berni, Jose A. and Buddenbaum, Henning and Hänchen, Lorenz},
year = {2024},
month = {08},
pages = {},
title = {Crop stress detection from UAVs: best practices and lessons learned for exploiting sensor synergies},
journal = {Precision Agriculture},
doi = {10.1007/s11119-024-10168-3},
URL = {https://link.springer.com/article/10.1007/s11119-024-10168-3},
}

M. Khanna, S. S. Atallah, T. Heckelei, L. Wu, and H. Storm, “Economics of the Adoption of Artificial Intelligence–Based Digital Technologies in Agriculture,” Annual Review of Resource Economics, 2024. doi:10.1146/annurev-resource-101623-092515
[BibTeX]

Rapid advances and diffusion of artificial intelligence (AI) technologies have the potential to transform agriculture globally by improving measurement, prediction, and site-specific management on the farm, enabling autonomous equipment that is trained to mimic human behavior and developing recommendation systems designed to autonomously achieve various tasks. Here, we discuss the applications of AI-enabled technologies in agriculture, including those that are capable of on-farm reinforcement learning and key attributes that distinguish them from precision technologies currently available. We then describe various ways through which AI-driven technologies are likely to change the decision space for farmers and require changes to the theoretical and empirical economic models that seek to understand the incentives for their adoption. We conclude with a discussion of areas for future research on the economic, environmental, and equity implications of AI-enabled technology adoption for the agricultural sector.

@article{annurev:/content/journals/10.1146/annurev-resource-101623-092515,
author = "Khanna, Madhu and Atallah, Shady S. and Heckelei, Thomas and Wu, Linghui and Storm, Hugo",
title = "Economics of the Adoption of Artificial Intelligence–Based Digital Technologies in Agriculture",
journal = "Annual Review of Resource Economics",
issn = "1941-1340",
year = "2024",
publisher = "Annual Reviews",
doi = "10.1146/annurev-resource-101623-092515",
abstract = "Rapid advances and diffusion of artificial intelligence (AI) technologies have the potential to transform agriculture globally by improving measurement, prediction, and site-specific management on the farm, enabling autonomous equipment that is trained to mimic human behavior and developing recommendation systems designed to autonomously achieve various tasks. Here, we discuss the applications of AI-enabled technologies in agriculture, including those that are capable of on-farm reinforcement learning and key attributes that distinguish them from precision technologies currently available. We then describe various ways through which AI-driven technologies are likely to change the decision space for farmers and require changes to the theoretical and empirical economic models that seek to understand the incentives for their adoption. We conclude with a discussion of areas for future research on the economic, environmental, and equity implications of AI-enabled technology adoption for the agricultural sector."
}

S. Pan, L. Jin, H. Hu, M. Popović, and M. Bennewitz, “How Many Views Are Needed to Reconstruct an Unknown Object Using NeRF?,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) , 2024, pp. 12470-12476. doi:10.1109/ICRA57147.2024.10610617
[BibTeX] [Code] [Video]

@INPROCEEDINGS{10610617,
author={Pan, Sicong and Jin, Liren and Hu, Hao and Popović, Marija and Bennewitz, Maren},
booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)},
title={How Many Views Are Needed to Reconstruct an Unknown Object Using NeRF?},
year={2024},
volume={},
number={},
pages={12470-12476},
keywords={Costs;Pipelines;Memory management;Data acquisition;Neural radiance field;Planning;Complexity theory},
doi={10.1109/ICRA57147.2024.10610617},
videourl={https://www.youtube.com/watch?v=LoQGOR3S1Fw},
codeurl={https://github.com/psc0628/NeRF-PRV}}

O. Knopf, A. Castro, J. Bendig, R. Pude, E. Kleist, H. Poorter, U. Rascher, and O. Muller, “Field phenotyping of ten wheat cultivars under elevated CO2 shows seasonal differences in chlorophyll fluorescence, plant height and vegetation indices,” Frontiers in Plant Science, vol. 14, 2024. doi:10.3389/fpls.2023.1304751
[BibTeX] [PDF]

In the context of climate change and global sustainable development goals, future wheat cultivation has to master various challenges at a time, including the rising atmospheric carbon dioxide concentration ([CO₂]). To investigate growth and photosynthesis dynamics under the effects of ambient (~434 ppm) and elevated [CO₂] (~622 ppm), a Free-Air CO₂ Enrichment (FACE) facility was combined with an automated phenotyping platform and an array of sensors. Ten modern winter wheat cultivars (Triticum aestivum L.) were monitored over a vegetation period using a Light-induced Fluorescence Transient (LIFT) sensor, ground-based RGB cameras and a UAV equipped with an RGB and multispectral camera. The LIFT sensor enabled a fast quantification of the photosynthetic performance by measuring the operating efficiency of Photosystem II (F_q’/F_m’) and the kinetics of electron transport, i.e. the reoxidation rates F_r1’ and F_r2’. Our results suggest that elevated [CO₂] significantly increased F_q’/F_m’ and plant height during the vegetative growth phase. As the plants transitioned to the senescence phase, a pronounced decline in F_q’/F_m’ was observed under elevated [CO₂]. This was also reflected in the reoxidation rates F_r1’ and F_r2’. A large majority of the cultivars showed a decrease in the harvest index, suggesting a different resource allocation and indicating a potential plateau in yield progression under e[CO₂]. Our results indicate that the rise in atmospheric [CO₂] has significant effects on the cultivation of winter wheat with strong manifestation during early and late growth.

@ARTICLE{10.3389/fpls.2023.1304751,
AUTHOR={Knopf, Oliver and Castro, Antony and Bendig, Juliane and Pude, Ralf and Kleist, Einhard and Poorter, Hendrik and Rascher, Uwe and Muller, Onno },
TITLE={Field phenotyping of ten wheat cultivars under elevated CO2 shows seasonal differences in chlorophyll fluorescence, plant height and vegetation indices},
JOURNAL={Frontiers in Plant Science},
VOLUME={14},
YEAR={2024},
URL={https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2023.1304751},
DOI={10.3389/fpls.2023.1304751},
ISSN={1664-462X},
ABSTRACT={In the context of climate change and global sustainable development goals, future wheat cultivation has to master various challenges at a time, including the rising atmospheric carbon dioxide concentration ([CO₂]). To investigate growth and photosynthesis dynamics under the effects of ambient (~434 ppm) and elevated [CO₂] (~622 ppm), a Free-Air CO₂ Enrichment (FACE) facility was combined with an automated phenotyping platform and an array of sensors. Ten modern winter wheat cultivars (Triticum aestivum L.) were monitored over a vegetation period using a Light-induced Fluorescence Transient (LIFT) sensor, ground-based RGB cameras and a UAV equipped with an RGB and multispectral camera. The LIFT sensor enabled a fast quantification of the photosynthetic performance by measuring the operating efficiency of Photosystem II (F_q’/F_m’) and the kinetics of electron transport, i.e. the reoxidation rates F_r1’ and F_r2’. Our results suggest that elevated [CO₂] significantly increased F_q’/F_m’ and plant height during the vegetative growth phase. As the plants transitioned to the senescence phase, a pronounced decline in F_q’/F_m’ was observed under elevated [CO₂]. This was also reflected in the reoxidation rates F_r1’ and F_r2’. A large majority of the cultivars showed a decrease in the harvest index, suggesting a different resource allocation and indicating a potential plateau in yield progression under e[CO₂]. Our results indicate that the rise in atmospheric [CO₂] has significant effects on the cultivation of winter wheat with strong manifestation during early and late growth.
}}

M. Theiß, A. Steier, U. Rascher, and M. Müller-Linow, “Completing the picture of field-grown cereal crops: a new method for detailed leaf surface models in wheat,” Plant Methods, vol. 20, 2024. doi:10.1186/s13007-023-01130-x
[BibTeX] [PDF]

@article{article,
author = {Theiß, Marie and Steier, Angelina and Rascher, Uwe and Müller-Linow, Mark},
year = {2024},
month = {02},
pages = {},
title = {Completing the picture of field-grown cereal crops: a new method for detailed leaf surface models in wheat},
volume = {20},
journal = {Plant Methods},
doi = {10.1186/s13007-023-01130-x},
url = {https://plantmethods.biomedcentral.com/articles/10.1186/s13007-023-01130-x}
}

M. Khan, S. Uhse, J. Bindics, B. Kogelmann, N. Nagarajan, R. Tabassum, K. D. Ingole, and A. Djamei, “Tip of the iceberg? Three novel TOPLESS-interacting effectors of the gall-inducing fungus Ustilago maydis,” New Phytologist, 2024. doi:10.1111/nph.19967
[BibTeX] [PDF]

Summary Ustilago maydis is a biotrophic pathogen causing smut disease in maize. It secretes a cocktail of effector proteins, which target different host proteins during its biotrophic stages in the host plant. One such class of proteins we identified previously is TOPLESS (TPL) and TOPLESS-RELATED (TPR) transcriptional corepressors. Here, we screened 297 U. maydis effector candidates for their ability to interact with maize TPL protein RAMOSA 1 ENHANCER LOCUS 2 LIKE 2 (RELK2) and their ability to induce auxin signaling and thereby identified three novel TPL-interacting protein effectors (Tip6, Tip7, and Tip8). Structural modeling and mutational analysis allowed the identification of TPL-interaction motifs of Tip6 and Tip7. In planta interaction between Tip6 and Tip7 with RELK2 occurs mainly in nuclear compartments, whereas Tip8 colocalizes with RELK2 in a compartment outside the nucleus. Overexpression of Tip8 in nonhost plants leads to cell death, indicating recognition of the effector or its activity. By performing infection assays with single and multideletion mutants of U. maydis, we demonstrate a positive role of Tip6 and Tip7 in U. maydis virulence. Transcriptional profiling of maize leaves infected with Tip effector mutants in comparison with SG200 strain suggests Tip effector activities are not merely redundant.

@article{https://doi.org/10.1111/nph.19967,
author = {Khan, Mamoona and Uhse, Simon and Bindics, Janos and Kogelmann, Benjamin and Nagarajan, Nithya and Tabassum, Riaz and Ingole, Kishor D. and Djamei, Armin},
title = {Tip of the iceberg? Three novel TOPLESS-interacting effectors of the gall-inducing fungus Ustilago maydis},
journal = {New Phytologist},
keywords = {auxin signaling, effector, galls, maize, RELK2, Tip, Topless corepressor, Ustilago maydis},
year = {2024},
doi = {10.1111/nph.19967},
url = {https://nph.onlinelibrary.wiley.com/doi/abs/10.1111/nph.19967},
eprint = {https://nph.onlinelibrary.wiley.com/doi/pdf/10.1111/nph.19967},
abstract = {Summary Ustilago maydis is a biotrophic pathogen causing smut disease in maize. It secretes a cocktail of effector proteins, which target different host proteins during its biotrophic stages in the host plant. One such class of proteins we identified previously is TOPLESS (TPL) and TOPLESS-RELATED (TPR) transcriptional corepressors. Here, we screened 297 U. maydis effector candidates for their ability to interact with maize TPL protein RAMOSA 1 ENHANCER LOCUS 2 LIKE 2 (RELK2) and their ability to induce auxin signaling and thereby identified three novel TPL-interacting protein effectors (Tip6, Tip7, and Tip8). Structural modeling and mutational analysis allowed the identification of TPL-interaction motifs of Tip6 and Tip7. In planta interaction between Tip6 and Tip7 with RELK2 occurs mainly in nuclear compartments, whereas Tip8 colocalizes with RELK2 in a compartment outside the nucleus. Overexpression of Tip8 in nonhost plants leads to cell death, indicating recognition of the effector or its activity. By performing infection assays with single and multideletion mutants of U. maydis, we demonstrate a positive role of Tip6 and Tip7 in U. maydis virulence. Transcriptional profiling of maize leaves infected with Tip effector mutants in comparison with SG200 strain suggests Tip effector activities are not merely redundant.}
}

N. Okole, F. R. Ispizua Yamati, R. Hossain, M. Varrelmann, A. Mahlein, and R. H. J. Heim, “Aerial low-altitude remote sensing and deep learning for in-field disease incidence scoring of virus yellows in sugar beet,” Plant Pathology, 2024. doi:10.1111/ppa.13973
[BibTeX] [PDF]

Abstract This study investigates the potential of high-resolution (<0.5 cm/pixel) aerial imagery and convolutional neural networks (CNNs) for disease incidence scoring in sugar beet, focusing on two important aphid-transmitted viruses, beet mild yellowing virus (BMYV) and beet chlorosis virus (BChV). The development of tolerant sugar beet cultivars is imperative in the context of increased disease management concerns due to the ban on neonicotinoids in the European Union. However, traditional methods of disease phenotyping, which rely on visual assessment by human experts, are both time-consuming and subjective. Therefore, this study assessed whether aerial multispectral and RGB images could be harnessed to perform automated disease ratings comparable to those performed by trained experts. To this end, two variety trials were conducted in 2021 and 2022. The 2021 dataset was used to train and validate a CNN model on five cultivars, while the 2022 dataset was used to test the model on two cultivars different from those used in 2021. Additionally, this study tests the use of transformed features instead of raw spectral bands to improve the generalization of CNN models. The results showed that the best CNN model was the one trained for BMYV on RGB images using transformed features instead of conventional raw bands. This model achieved a root mean square error score of 11.45\% between the model and expert scores. These results indicate that while high-resolution aerial imagery and CNNs hold great promise, a complete replacement of human expertise is not yet possible. This research contributes to an innovative approach to disease phenotyping, driving advances in sustainable agriculture and crop breeding.

@article{https://doi.org/10.1111/ppa.13973,
author = {Okole, Nathan and Ispizua Yamati, Facundo R. and Hossain, Roxana and Varrelmann, Mark and Mahlein, Anne-Katrin and Heim, Rene H. J.},
title = {Aerial low-altitude remote sensing and deep learning for in-field disease incidence scoring of virus yellows in sugar beet},
journal = {Plant Pathology},
keywords = {convolutional neural networks, phenotyping, unmanned aerial vehicle, virus yellows},
year = {2024},
doi = {10.1111/ppa.13973},
url = {https://www.phenorob.de/wp-content/uploads/2024/08/Okole_2024_PlPathol_10-1111-ppa-13973.pdf},
abstract = {Abstract This study investigates the potential of high-resolution (<0.5 cm/pixel) aerial imagery and convolutional neural networks (CNNs) for disease incidence scoring in sugar beet, focusing on two important aphid-transmitted viruses, beet mild yellowing virus (BMYV) and beet chlorosis virus (BChV). The development of tolerant sugar beet cultivars is imperative in the context of increased disease management concerns due to the ban on neonicotinoids in the European Union. However, traditional methods of disease phenotyping, which rely on visual assessment by human experts, are both time-consuming and subjective. Therefore, this study assessed whether aerial multispectral and RGB images could be harnessed to perform automated disease ratings comparable to those performed by trained experts. To this end, two variety trials were conducted in 2021 and 2022. The 2021 dataset was used to train and validate a CNN model on five cultivars, while the 2022 dataset was used to test the model on two cultivars different from those used in 2021. Additionally, this study tests the use of transformed features instead of raw spectral bands to improve the generalization of CNN models. The results showed that the best CNN model was the one trained for BMYV on RGB images using transformed features instead of conventional raw bands. This model achieved a root mean square error score of 11.45\% between the model and expert scores. These results indicate that while high-resolution aerial imagery and CNNs hold great promise, a complete replacement of human expertise is not yet possible. This research contributes to an innovative approach to disease phenotyping, driving advances in sustainable agriculture and crop breeding.}
}

S. Wunder, D. Schulz, J. G. Montoya-Zumaeta, J. Börner, G. Frey, and B. Betancur-Corredor, "Modest forest and welfare gains from initiatives for reduced emissions from deforestation and forest degradation," Communications Earth & Environment, vol. 5, 2024. doi:10.1038/s43247-024-01541-1
[BibTeX] [PDF]

@article{article,
author = {Wunder, Sven and Schulz, Dario and Montoya-Zumaeta, Javier G. and Börner, Jan and Frey, Gabriel and Betancur-Corredor, Bibiana},
year = {2024},
month = {07},
pages = {},
title = {Modest forest and welfare gains from initiatives for reduced emissions from deforestation and forest degradation},
volume = {5},
journal = {Communications Earth & Environment},
url = {https://www.nature.com/articles/s43247-024-01541-1.pdf},
doi = {10.1038/s43247-024-01541-1}
}

V. Moudrý, M. Bazzichetto, R. Remelgado, R. Devillers, J. Lenoir, R. G. Mateo, J. J. Lembrechts, N. Sillero, V. Lecours, A. F. Cord, V. Barták, P. Balej, D. Rocchini, M. Torresani, S. Arenas-Castro, M. Man, D. Prajzlerová, K. Gdulová, J. Prošek, E. Marchetto, A. Zarzo-Arias, L. Gábor, F. Leroy, M. Martini, M. Malavasi, R. Cazzolla Gatti, J. Wild, and P. Šímová, "Optimising occurrence data in species distribution models: sample size, positional uncertainty, and sampling bias matter," Ecography, p. e07294, 2024. doi:10.1111/ecog.07294
[BibTeX] [PDF]

Species distribution models (SDMs) have proven valuable in filling gaps in our knowledge of species occurrences. However, despite their broad applicability, SDMs exhibit critical shortcomings due to limitations in species occurrence data. These limitations include, in particular, issues related to sample size, positional uncertainty, and sampling bias. In addition, it is widely recognised that the quality of SDMs as well as the approaches used to mitigate the impact of the aforementioned data limitations depend on species ecology. While numerous studies have evaluated the effects of these data limitations on SDM performance, a synthesis of their results is lacking. However, without a comprehensive understanding of their individual and combined effects, our ability to predict the influence of these issues on the quality of modelled species–environment associations remains largely uncertain, limiting the value of model outputs. In this paper, we review studies that have evaluated the effects of sample size, positional uncertainty, sampling bias, and species ecology on SDMs outputs. We build upon their findings to provide recommendations for the critical assessment of species data intended for use in SDMs.

@article{https://doi.org/10.1111/ecog.07294,
author = {Moudrý, Vítězslav and Bazzichetto, Manuele and Remelgado, Ruben and Devillers, Rodolphe and Lenoir, Jonathan and Mateo, Rubén G. and Lembrechts, Jonas J. and Sillero, Neftalí and Lecours, Vincent and Cord, Anna F. and Barták, Vojtěch and Balej, Petr and Rocchini, Duccio and Torresani, Michele and Arenas-Castro, Salvador and Man, Matěj and Prajzlerová, Dominika and Gdulová, Kateřina and Prošek, Jiří and Marchetto, Elisa and Zarzo-Arias, Alejandra and Gábor, Lukáš and Leroy, François and Martini, Matilde and Malavasi, Marco and Cazzolla Gatti, Roberto and Wild, Jan and Šímová, Petra},
title = {Optimising occurrence data in species distribution models: sample size, positional uncertainty, and sampling bias matter},
journal = {Ecography},
year = {2024},
pages = {e07294},
keywords = {data quality, ecological niche modelling, filtering, sampling, spatial scale, validation},
doi = {10.1111/ecog.07294},
url = {https://nsojournals.onlinelibrary.wiley.com/doi/abs/10.1111/ecog.07294},
eprint = {https://nsojournals.onlinelibrary.wiley.com/doi/pdf/10.1111/ecog.07294},
abstract = {Species distribution models (SDMs) have proven valuable in filling gaps in our knowledge of species occurrences. However, despite their broad applicability, SDMs exhibit critical shortcomings due to limitations in species occurrence data. These limitations include, in particular, issues related to sample size, positional uncertainty, and sampling bias. In addition, it is widely recognised that the quality of SDMs as well as the approaches used to mitigate the impact of the aforementioned data limitations depend on species ecology. While numerous studies have evaluated the effects of these data limitations on SDM performance, a synthesis of their results is lacking. However, without a comprehensive understanding of their individual and combined effects, our ability to predict the influence of these issues on the quality of modelled species–environment associations remains largely uncertain, limiting the value of model outputs. In this paper, we review studies that have evaluated the effects of sample size, positional uncertainty, sampling bias, and species ecology on SDMs outputs. We build upon their findings to provide recommendations for the critical assessment of species data intended for use in SDMs.}
}

P. Gutbrod, D. Pottier, S. Shirvani, K. Gutbrod, F. Djien-Nyami, R. Emade Ngoudjede, G. Ngando-Ebongue, and P. Dörmann, "Unusual vitamin E profile in the oil of a wild African oil palm tree (Elaeis guineensis Jacq.) enhances oxidative stability of provitamin A," Frontiers in Plant Science, vol. 15, 2024. doi:10.3389/fpls.2024.1400852
[BibTeX] [PDF]

Introduction

The African oil palm (Elaeis guineensis Jacq.) is the predominant oil crop in the world. In addition to triacylglycerols, crude palm oil (CPO) extracted from the mesocarp of the fruits, contains high amounts of provitamin A (carotenes) and vitamin E (tocochromanols). Because of their unsaturated nature, the carotenes are prone to oxidation and therefore are in part limiting for the shelf life of CPO.

Methods

A tree with unusual toochromanol composition was identified by HPLC screening of the mesocarp of wild trees. Polymorphisms in a candidate gene were identified by DNA sequencing. The candidate protein was heterologously expressed in Escherichia coli coli and Arabidopsis thaliana to test for enzyme activity. Oxidative stability of the CPO was studied by following carotene degradation over time.

Results

In the present study, a wild Oil Palm tree (C59) from Cameroon was identified that lacks α-tocopherol and α-tocotrienol and instead accumulates the respective γ forms, suggesting that the activity of γ-tocopherol methyltransferase (VTE4) was affected. Sequencing of the VTE4 locus in the genome of plant C59 identified a G/C polymorphism that causes the exchange of a highly conserved tryptophan at position 290 with serine. The W290S exchange renders the VTE4 enzyme inactive, as shown after expression in Escherichia coli and Arabidopsis thaliana. The oxidative stability of carotenes in the mesocarp of the wild palm C59 was enhanced compared with control accessions. Furthermore, supplementation of commercial palm oil with different tocochromanols showed that γ-tocotrienol exerts a stronger effect during the protection of carotenes against oxidation than α-tocotrienol.

Discussion

Therefore, the introduction of the high γ-tocotrienol trait into elite breeding lines represents a potent strategy to protect carotenes against oxidation and extend the shelf life of CPO, hence allowing the development of a value added high-carotene CPO to be used to fight against vitamin A deficiency.

@ARTICLE{10.3389/fpls.2024.1400852,
AUTHOR={Gutbrod, Philipp and Pottier, Delphine and Shirvani, Safoora and Gutbrod, Katharina and Djien-Nyami, Félicité and Emade Ngoudjede, Raïssa and Ngando-Ebongue, Georges and Dörmann, Peter },
TITLE={Unusual vitamin E profile in the oil of a wild African oil palm tree (Elaeis guineensis Jacq.) enhances oxidative stability of provitamin A},
JOURNAL={Frontiers in Plant Science},
VOLUME={15},
YEAR={2024},
URL={https://www.phenorob.de/wp-content/uploads/2024/08/Gutbrod-Doermann-2024-FrontPlantSci-african-oil-palm-tocopherol.pdf},
DOI={10.3389/fpls.2024.1400852},
ISSN={1664-462X},
ABSTRACT={IntroductionThe African oil palm (Elaeis guineensis Jacq.) is the predominant oil crop in the world. In addition to triacylglycerols, crude palm oil (CPO) extracted from the mesocarp of the fruits, contains high amounts of provitamin A (carotenes) and vitamin E (tocochromanols). Because of their unsaturated nature, the carotenes are prone to oxidation and therefore are in part limiting for the shelf life of CPO.MethodsA tree with unusual toochromanol composition was identified by HPLC screening of the mesocarp of wild trees. Polymorphisms in a candidate gene were identified by DNA sequencing. The candidate protein was heterologously expressed in Escherichia coli coli and Arabidopsis thaliana to test for enzyme activity. Oxidative stability of the CPO was studied by following carotene degradation over time.ResultsIn the present study, a wild Oil Palm tree (C59) from Cameroon was identified that lacks α-tocopherol and α-tocotrienol and instead accumulates the respective γ forms, suggesting that the activity of γ-tocopherol methyltransferase (VTE4) was affected. Sequencing of the VTE4 locus in the genome of plant C59 identified a G/C polymorphism that causes the exchange of a highly conserved tryptophan at position 290 with serine. The W290S exchange renders the VTE4 enzyme inactive, as shown after expression in Escherichia coli and Arabidopsis thaliana. The oxidative stability of carotenes in the mesocarp of the wild palm C59 was enhanced compared with control accessions. Furthermore, supplementation of commercial palm oil with different tocochromanols showed that γ-tocotrienol exerts a stronger effect during the protection of carotenes against oxidation than α-tocotrienol.DiscussionTherefore, the introduction of the high γ-tocotrienol trait into elite breeding lines represents a potent strategy to protect carotenes against oxidation and extend the shelf life of CPO, hence allowing the development of a value added high-carotene CPO to be used to fight against vitamin A deficiency.}}

P. Martre, S. Dueri, J. Guarin, F. Ewert, H. Webber, D. Calderini, G. Molero, M. Reynolds, D. Miralles, G. García, H. Brown, M. George, R. Craigie, J. Cohan, J. Deswarte, G. Slafer, F. Giunta, D. Cammarano, R. Ferrise, and S. Asseng, "Global needs for nitrogen fertilizer to improve wheat yield under climate change," Nature Plants, vol. 10, pp. 1-10, 2024. doi:10.1038/s41477-024-01739-3
[BibTeX]

@article{article,
author = {Martre, Pierre and Dueri, Sibylle and Guarin, Jose and Ewert, Frank and Webber, Heidi and Calderini, Daniel and Molero, Gemma and Reynolds, Matthew and Miralles, Daniel and García, Guillermo and Brown, Hamish and George, Mike and Craigie, Rob and Cohan, Jean-Pierre and Deswarte, Jean-Charles and Slafer, Gustavo and Giunta, Francesco and Cammarano, Davide and Ferrise, Roberto and Asseng, Senthold},
year = {2024},
month = {07},
pages = {1-10},
title = {Global needs for nitrogen fertilizer to improve wheat yield under climate change},
volume = {10},
journal = {Nature Plants},
doi = {10.1038/s41477-024-01739-3},
}

H. H. Zeddies, G. Busch, and M. Qaim, "Positive public attitudes towards agricultural robots," Scientific Reports, vol. 14, 2024. doi:10.1038/s41598-024-66198-4
[BibTeX] [PDF] [Video]

@article{article,
author = {Zeddies, Hendrik Hilmar and Busch, Gesa and Qaim, Matin},
year = {2024},
title = {Positive public attitudes towards agricultural robots},
volume = {14},
journal = {Scientific Reports},
doi = {10.1038/s41598-024-66198-4},
videourl = {https://www.youtube.com/watch?v=UVyvPGpRDy8},
url = {https://pubmed.ncbi.nlm.nih.gov/38971894/}
}

D. Wuepper, I. Wiebecke, L. Meier, S. Vogelsanger, S. Bramato, A. Fürholz, and R. Finger, "Agri-environmental policies from 1960 to 2022," Nature Food, vol. 5, pp. 1-9, 2024. doi:10.1038/s43016-024-00945-8
[BibTeX] [PDF]

@article{article,
author = {Wuepper, David and Wiebecke, Ilsabe and Meier, Lara and Vogelsanger, Sarah and Bramato, Selina and Fürholz, Andrea and Finger, Robert},
year = {2024},
month = {03},
pages = {1-9},
title = {Agri-environmental policies from 1960 to 2022},
volume = {5},
journal = {Nature Food},
doi = {10.1038/s43016-024-00945-8},
url = {https://www.nature.com/articles/s43016-024-00945-8.pdf}
}

H. Storm, T. Heckelei, and K. Baylis, "Probabilistic programming for embedding theory and quantifying uncertainty in econometric analysis," European Review of Agricultural Economics, p. jbae016, 2024. doi:10.1093/erae/jbae016
[BibTeX] [PDF]

{The replication crisis in empirical research calls for a more mindful approach to how we apply and report statistical models. For empirical research to have a lasting (policy) impact, these concerns are crucial. In this paper, we present Probabilistic Programming (PP) as a way forward. The PP workflow with an explicit data-generating process enhances the communication of model assumptions, code testing and consistency between theory and estimation. By simplifying Bayesian analysis, it also offers advantages for the interpretation, communication and modelling of uncertainty. We outline the advantages of PP to encourage its adoption in our community.}

@article{10.1093/erae/jbae016,
author = {Storm, Hugo and Heckelei, Thomas and Baylis, Kathy},
title = "{Probabilistic programming for embedding theory and quantifying uncertainty in econometric analysis}",
journal = {European Review of Agricultural Economics},
pages = {jbae016},
year = {2024},
month = {07},
abstract = "{The replication crisis in empirical research calls for a more mindful approach to how we apply and report statistical models. For empirical research to have a lasting (policy) impact, these concerns are crucial. In this paper, we present Probabilistic Programming (PP) as a way forward. The PP workflow with an explicit data-generating process enhances the communication of model assumptions, code testing and consistency between theory and estimation. By simplifying Bayesian analysis, it also offers advantages for the interpretation, communication and modelling of uncertainty. We outline the advantages of PP to encourage its adoption in our community.}",
issn = {0165-1587},
doi = {10.1093/erae/jbae016},
url = {https://academic.oup.com/erae/advance-article-pdf/doi/10.1093/erae/jbae016/58402411/jbae016.pdf},
eprint = {https://academic.oup.com/erae/advance-article-pdf/doi/10.1093/erae/jbae016/58402411/jbae016.pdf},
}

R. Ogawa, J. O. Engler, and A. F. Cord, "Functional responses in habitat selection as a management tool to evaluate agri-environment schemes for farmland birds," Ecological Modelling, vol. 494, p. 110778, 2024. doi:10.1016/j.ecolmodel.2024.110778
[BibTeX] [PDF]

Agri-environment schemes (AES), as part of the European Union's Common Agricultural Policy, are intended to help prevent the decline of farmland biodiversity. Nevertheless, the ecological effectiveness of AES in supporting farmland bird populations remains inconclusive across studies. This inconsistency highlights a research gap: What behavioral mechanisms contribute to the variation in farmland bird populations? This variability may arise because farmland birds alter their habitat selection in response to available habitat—a phenomenon known as functional responses in habitat selection. Here, we examined the effects of AES and non-AES land-use variables on habitat selection of farmland birds, taking into account the species-specific functional response to availability. We built two types of hierarchical distance sampling models to analyze observational data of four farmland bird species from line-transect surveys during the breeding season in Saxony, Germany. First, we built mixed-effects models to estimate the marginal effects of AES and non-AES land-use variables on the occurrence of farmland birds. Second, we integrated linear models into the distance sampling model to relate habitat selection estimates to habitat availability. Results from the first mixed-effects model showed positive and negative effects of AES on habitat selection. In the second model, we observed inverse relationships between habitat selection and availability for both AES and non-AES variables. These results support the hypothesis of negative functional responses, as we found a decrease in the tendency of farmland birds to select both AES and non-AES land uses as their availability increased. Our findings suggest that the varying effects of AES on bird occurrences reported in the literature may depend on cross-study differences in AES availability. We propose that functional responses in habitat selection should be considered as a phenomenon in future AES research. Our study also highlights the importance of optimal AES provision and their spatial allocation in the agricultural landscape.

@article{OGAWA2024110778,
title = {Functional responses in habitat selection as a management tool to evaluate agri-environment schemes for farmland birds},
journal = {Ecological Modelling},
volume = {494},
pages = {110778},
year = {2024},
issn = {0304-3800},
doi = {10.1016/j.ecolmodel.2024.110778},
url = {https://www.sciencedirect.com/science/article/pii/S0304380024001662?via%3Dihub},
author = {Ryo Ogawa and Jan O. Engler and Anna F. Cord},
keywords = {Agricultural landscape, Bayesian statistics, Behavioral trade-off, Biodiversity conservation, Distance sampling, Farmland birds},
abstract = {Agri-environment schemes (AES), as part of the European Union's Common Agricultural Policy, are intended to help prevent the decline of farmland biodiversity. Nevertheless, the ecological effectiveness of AES in supporting farmland bird populations remains inconclusive across studies. This inconsistency highlights a research gap: What behavioral mechanisms contribute to the variation in farmland bird populations? This variability may arise because farmland birds alter their habitat selection in response to available habitat—a phenomenon known as functional responses in habitat selection. Here, we examined the effects of AES and non-AES land-use variables on habitat selection of farmland birds, taking into account the species-specific functional response to availability. We built two types of hierarchical distance sampling models to analyze observational data of four farmland bird species from line-transect surveys during the breeding season in Saxony, Germany. First, we built mixed-effects models to estimate the marginal effects of AES and non-AES land-use variables on the occurrence of farmland birds. Second, we integrated linear models into the distance sampling model to relate habitat selection estimates to habitat availability. Results from the first mixed-effects model showed positive and negative effects of AES on habitat selection. In the second model, we observed inverse relationships between habitat selection and availability for both AES and non-AES variables. These results support the hypothesis of negative functional responses, as we found a decrease in the tendency of farmland birds to select both AES and non-AES land uses as their availability increased. Our findings suggest that the varying effects of AES on bird occurrences reported in the literature may depend on cross-study differences in AES availability. We propose that functional responses in habitat selection should be considered as a phenomenon in future AES research. Our study also highlights the importance of optimal AES provision and their spatial allocation in the agricultural landscape.}
}

T. Václavík, M. Beckmann, M. Bednář, S. Brdar, G. Breckenridge, A. F. Cord, C. Domingo-Marimon, A. Gosal, F. Langerwisch, A. Paulus, S. Roilo, B. Šarapatka, G. Ziv, and T. Čejka, "Farming system archetypes help explain the uptake of agri-environment practices in Europe," Environmental Research Letters, vol. 19, iss. 7, p. 74004, 2024. doi:10.1088/1748-9326/ad4efa
[BibTeX] [PDF]

The adoption of agri-environment practices (AEPs) is crucial for safeguarding the long-term sustainability of ecosystem services within European agricultural landscapes. However, the tailoring of agri-environment policies to the unique characteristics of farming systems is a challenging task, often neglecting local farm parameters or requiring extensive farm survey data. Here, we develop a simplified typology of farming system archetypes (FSAs), using field-level data on farms’ economic size and specialisation derived from the Integrated Administration and Control System in three case studies in Germany, Czechia and the United Kingdom. Our typology identifies groups of farms that are assumed to react similarly to agricultural policy measures, bridging the gap between efforts to understand individual farm behaviour and broad agri-environmental typologies. We assess the usefulness of our approach by quantifying the spatial association of identified archetypes of farming systems with ecologically relevant AEPs (cover crops, fallow, organic farming, grassland maintenance, vegetation buffers, conversion of cropland to grassland and forest) to understand the rates of AEP adoption by different types of farms. Our results show that of the 20 archetypes, economically large farms specialised in general cropping dominate the agricultural land in all case studies, covering 56% to 85% of the total agricultural area. Despite regional differences, we found consistent trends in AEP adoption across diverse contexts. Economically large farms and those specialising in grazing livestock were more likely to adopt AEPs, with economically larger farms demonstrating a proclivity for a wider range of measures. In contrast, economically smaller farms usually focused on a narrower spectrum of AEPs and, together with farms with an economic value <2 000 EUR, accounted for 70% of all farms with no AEP uptake. These insights indicate the potential of the FSA typology as a framework to infer key patterns of AEP adoption, thus providing relevant information to policy-makers for more direct identification of policy target groups and ultimately for developing more tailored agri-environment policies.

@article{Václavík_2024,
doi = {10.1088/1748-9326/ad4efa},
url = {https://dx.doi.org/10.1088/1748-9326/ad4efa},
year = {2024},
month = {jun},
publisher = {IOP Publishing},
volume = {19},
number = {7},
pages = {074004},
author = {Tomáš Václavík and Michael Beckmann and Marek Bednář and Sanja Brdar and George Breckenridge and Anna F Cord and Cristina Domingo-Marimon and Arjan Gosal and Fanny Langerwisch and Anne Paulus and Stephanie Roilo and Bořivoj Šarapatka and Guy Ziv and Tomáš Čejka},
title = {Farming system archetypes help explain the uptake of agri-environment practices in Europe},
journal = {Environmental Research Letters},
abstract = {The adoption of agri-environment practices (AEPs) is crucial for safeguarding the long-term sustainability of ecosystem services within European agricultural landscapes. However, the tailoring of agri-environment policies to the unique characteristics of farming systems is a challenging task, often neglecting local farm parameters or requiring extensive farm survey data. Here, we develop a simplified typology of farming system archetypes (FSAs), using field-level data on farms’ economic size and specialisation derived from the Integrated Administration and Control System in three case studies in Germany, Czechia and the United Kingdom. Our typology identifies groups of farms that are assumed to react similarly to agricultural policy measures, bridging the gap between efforts to understand individual farm behaviour and broad agri-environmental typologies. We assess the usefulness of our approach by quantifying the spatial association of identified archetypes of farming systems with ecologically relevant AEPs (cover crops, fallow, organic farming, grassland maintenance, vegetation buffers, conversion of cropland to grassland and forest) to understand the rates of AEP adoption by different types of farms. Our results show that of the 20 archetypes, economically large farms specialised in general cropping dominate the agricultural land in all case studies, covering 56% to 85% of the total agricultural area. Despite regional differences, we found consistent trends in AEP adoption across diverse contexts. Economically large farms and those specialising in grazing livestock were more likely to adopt AEPs, with economically larger farms demonstrating a proclivity for a wider range of measures. In contrast, economically smaller farms usually focused on a narrower spectrum of AEPs and, together with farms with an economic value <2 000 EUR, accounted for 70% of all farms with no AEP uptake. These insights indicate the potential of the FSA typology as a framework to infer key patterns of AEP adoption, thus providing relevant information to policy-makers for more direct identification of policy target groups and ultimately for developing more tailored agri-environment policies.}
}

J. Bömer, F. Esser, E. Marks, R. A. Rosu, S. Behnke, L. Klingbeil, H. Kuhlmann, C. Stachniss, A. Mahlein, and S. Paulus, "A 3D printed plant model for accurate and reliable 3D plant phenotyping," GigaScience, vol. 13, p. giae035, 2024. doi:10.1093/gigascience/giae035
[BibTeX] [PDF]

{This study addresses the importance of precise referencing in 3-dimensional (3D) plant phenotyping, which is crucial for advancing plant breeding and improving crop production. Traditionally, reference data in plant phenotyping rely on invasive methods. Recent advancements in 3D sensing technologies offer the possibility to collect parameters that cannot be referenced by manual measurements. This work focuses on evaluating a 3D printed sugar beet plant model as a referencing tool.Fused deposition modeling has turned out to be a suitable 3D printing technique for creating reference objects in 3D plant phenotyping. Production deviations of the created reference model were in a low and acceptable range. We were able to achieve deviations ranging from −10 mm to +5 mm. In parallel, we demonstrated a high-dimensional stability of the reference model, reaching only ±4 mm deformation over the course of 1 year. Detailed print files, assembly descriptions, and benchmark parameters are provided, facilitating replication and benefiting the research community.Consumer-grade 3D printing was utilized to create a stable and reproducible 3D reference model of a sugar beet plant, addressing challenges in referencing morphological parameters in 3D plant phenotyping. The reference model is applicable in 3 demonstrated use cases: evaluating and comparing 3D sensor systems, investigating the potential accuracy of parameter extraction algorithms, and continuously monitoring these algorithms in practical experiments in greenhouse and field experiments. Using this approach, it is possible to monitor the extraction of a nonverifiable parameter and create reference data. The process serves as a model for developing reference models for other agricultural crops.}

@article{10.1093/gigascience/giae035,
author = {Bömer, Jonas and Esser, Felix and Marks, Elias and Rosu, Radu Alexandru and Behnke, Sven and Klingbeil, Lasse and Kuhlmann, Heiner and Stachniss, Cyrill and Mahlein, Anne-Katrin and Paulus, Stefan},
title = "{A 3D printed plant model for accurate and reliable 3D plant phenotyping}",
journal = {GigaScience},
volume = {13},
pages = {giae035},
year = {2024},
month = {06},
abstract = "{This study addresses the importance of precise referencing in 3-dimensional (3D) plant phenotyping, which is crucial for advancing plant breeding and improving crop production. Traditionally, reference data in plant phenotyping rely on invasive methods. Recent advancements in 3D sensing technologies offer the possibility to collect parameters that cannot be referenced by manual measurements. This work focuses on evaluating a 3D printed sugar beet plant model as a referencing tool.Fused deposition modeling has turned out to be a suitable 3D printing technique for creating reference objects in 3D plant phenotyping. Production deviations of the created reference model were in a low and acceptable range. We were able to achieve deviations ranging from −10 mm to +5 mm. In parallel, we demonstrated a high-dimensional stability of the reference model, reaching only ±4 mm deformation over the course of 1 year. Detailed print files, assembly descriptions, and benchmark parameters are provided, facilitating replication and benefiting the research community.Consumer-grade 3D printing was utilized to create a stable and reproducible 3D reference model of a sugar beet plant, addressing challenges in referencing morphological parameters in 3D plant phenotyping. The reference model is applicable in 3 demonstrated use cases: evaluating and comparing 3D sensor systems, investigating the potential accuracy of parameter extraction algorithms, and continuously monitoring these algorithms in practical experiments in greenhouse and field experiments. Using this approach, it is possible to monitor the extraction of a nonverifiable parameter and create reference data. The process serves as a model for developing reference models for other agricultural crops.}",
issn = {2047-217X},
doi = {10.1093/gigascience/giae035},
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/boemer2024giga.pdf},
eprint = {https://academic.oup.com/gigascience/article-pdf/doi/10.1093/gigascience/giae035/58270533/giae035.pdf},
}

L. Drees, D. T. Demie, M. Paul, S. Seidel, T. Döring, and R. Roscher, "Data-driven Crop Growth Simulation on Time-varying Generated Images using Multi-conditional Generative Adversarial Networks," Plant Methods, 2024.
[BibTeX] [PDF] [Code]

@article{dreescropgrowth,
author={Lukas Drees and Dereje T. Demie and Madhuri Paul and Sabine Seidel and Thomas Döring and Ribana Roscher},
title={Data-driven Crop Growth Simulation on Time-varying Generated Images using Multi-conditional Generative Adversarial Networks},
journal = {Plant Methods},
year={2024},
codeurl={https://github.com/luked12/crop-growth-cgan},
url ={https://plantmethods.biomedcentral.com/articles/10.1186/s13007-024-01205-3}
}

D. Wuepper, T. Crowther, T. Lauber, D. Routh, S. Le Clec'h, R. D. Garrett, and J. Börner, "Public policies and global forest conservation: Empirical evidence from national borders," Global Environmental Change, vol. 84, p. 102770, 2024. doi:10.1016/j.gloenvcha.2023.102770
[BibTeX] [PDF]

Protecting the world’s remaining forests is a global policy priority. Even though the value of the world’s remaining forests is global in nature, much of the protection has to come from national policies. Here, we combine global, high resolution remote sensing data on forest outcomes (tree-cover loss, forest degradation, net primary production) and two complementary econometric research designs for causal inference to first quantify how much it matters in which country a forest is located, secondly, the role of public policies, and third, under which conditions such pubic policies tend to be most successful. We find considerable border discontinuities in remotely sensed forest outcomes around the world (in a regression discontinuity design) and these are largely explained by countries’ policies (using a differences-in-discontinuities design). We estimate that public policies reduce the risk of tree cover loss by almost 4 percentage points globally, but there is large variation around this. The best explanations we find for these heterogenous treatment effects are a country’s policy enforcement, its policy stringency, its property rights, and its rule of law (in that order). Our results motivate international cooperation to finance and improve (a) countries’ public policies for forest protection and (b) countries’ capacity to implement and enforce them well.

@article{WUEPPER2024102770,
title = {Public policies and global forest conservation: Empirical evidence from national borders},
journal = {Global Environmental Change},
volume = {84},
pages = {102770},
year = {2024},
issn = {0959-3780},
doi = {10.1016/j.gloenvcha.2023.102770},
url = {https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/649520/1-s2.0-S095937802300136X-main.pdf?sequence=1&isAllowed=y},
author = {David Wuepper and Thomas Crowther and Thomas Lauber and Devin Routh and Solen {Le Clec'h} and Rachael D. Garrett and Jan Börner},
keywords = {Tree-cover loss, Forest degradation, Net primary production, National policies, Difference-in-discontinuities design},
abstract = {Protecting the world’s remaining forests is a global policy priority. Even though the value of the world’s remaining forests is global in nature, much of the protection has to come from national policies. Here, we combine global, high resolution remote sensing data on forest outcomes (tree-cover loss, forest degradation, net primary production) and two complementary econometric research designs for causal inference to first quantify how much it matters in which country a forest is located, secondly, the role of public policies, and third, under which conditions such pubic policies tend to be most successful. We find considerable border discontinuities in remotely sensed forest outcomes around the world (in a regression discontinuity design) and these are largely explained by countries’ policies (using a differences-in-discontinuities design). We estimate that public policies reduce the risk of tree cover loss by almost 4 percentage points globally, but there is large variation around this. The best explanations we find for these heterogenous treatment effects are a country’s policy enforcement, its policy stringency, its property rights, and its rule of law (in that order). Our results motivate international cooperation to finance and improve (a) countries’ public policies for forest protection and (b) countries’ capacity to implement and enforce them well.}
}

M. Groß, M. Mail, O. Wrigley, R. Debastiani, T. Scherer, W. Amelung, and M. Braun, "Plastic Fruit Stickers in Industrial Composting─Surface and Structural Alterations Revealed by Electron Microscopy and Computed Tomography," Environmental Science & Technology, vol. 58, iss. 16, pp. 7124-7132, 2024. doi:10.1021/acs.est.3c08734
[BibTeX] [PDF]

@article{doi:10.1021/acs.est.3c08734,
author = {Groß, Max and Mail, Matthias and Wrigley, Olivia and Debastiani, Rafaela and Scherer, Torsten and Amelung, Wulf and Braun, Melanie},
title = {Plastic Fruit Stickers in Industrial Composting─Surface and Structural Alterations Revealed by Electron Microscopy and Computed Tomography},
journal = {Environmental Science \& Technology},
volume = {58},
number = {16},
pages = {7124-7132},
year = {2024},
doi = {10.1021/acs.est.3c08734},
note ={PMID: 38599582},
URL = {
https://doi.org/10.1021/acs.est.3c08734
},
eprint = {
https://doi.org/10.1021/acs.est.3c08734
}
}

S. Pan, L. Jin, X. Huang, C. Stachniss, M. Popović, and M. Bennewitz, Exploiting Priors from 3D Diffusion Models for RGB-Based One-Shot View Planning, 2024.
[BibTeX] [PDF]

@misc{pan2024exploiting,
title={Exploiting Priors from 3D Diffusion Models for RGB-Based One-Shot View Planning},
author={Sicong Pan and Liren Jin and Xuying Huang and Cyrill Stachniss and Marija Popović and Maren Bennewitz},
year={2024},
eprint={2403.16803},
archivePrefix={arXiv},
primaryClass={id='cs.RO' full_name='Robotics' is_active=True alt_name=None in_archive='cs' is_general=False description='Roughly includes material in ACM Subject Class I.2.9.'},
url={https://arxiv.org/abs/2403.16803}
}

S. S. Dogar, C. Brogi, H. Vereecken, and J. Huisman, "Data fusion and classification of electromagnetic induction and remote sensing data for management zone delineation in sustainable agriculture," , 2024. doi:10.5194/egusphere-egu24-16241
[BibTeX] [PDF]

@unknown{unknown,
author = {Dogar, Salar Saeed and Brogi, Cosimo and Vereecken, Harry and Huisman, Johan},
year = {2024},
month = {04},
pages = {},
title = {Data fusion and classification of electromagnetic induction and remote sensing data for management zone delineation in sustainable agriculture},
doi = {10.5194/egusphere-egu24-16241},
url = {https://www.researchgate.net/publication/379840614_Data_fusion_and_classification_of_electromagnetic_induction_and_remote_sensing_data_for_management_zone_delineation_in_sustainable_agriculture}
}

H. Hu, S. Pan, L. Jin, M. Popović, and M. Bennewitz, Active Implicit Reconstruction Using One-Shot View Planning, 2024. doi:10.48550/arXiv.2310.00685
[BibTeX] [PDF] [Code] [Video]

@misc{hu2024active,
title={Active Implicit Reconstruction Using One-Shot View Planning},
author={Hao Hu and Sicong Pan and Liren Jin and Marija Popović and Maren Bennewitz},
year={2024},
eprint={2310.00685},
archivePrefix={arXiv},
primaryClass={id='cs.RO' full_name='Robotics' is_active=True alt_name=None in_archive='cs' is_general=False description='Roughly includes material in ACM Subject Class I.2.9.'},
codeurl={https://github.com/psc0628/AIR-OSVP},
videourl={https://www.youtube.com/watch?v=h24cc2wYAoc},
url={https://arxiv.org/pdf/2310.00685},
doi={10.48550/arXiv.2310.00685}
}

C. Lenz, R. Menon, M. Schreiber, M. P. Jacob, S. Behnke, and M. Bennewitz, HortiBot: An Adaptive Multi-Arm System for Robotic Horticulture of Sweet Peppers, 2024. doi:10.48550/arXiv.2403.15306
[BibTeX] [PDF]

@misc{lenz2024hortibot,
title={HortiBot: An Adaptive Multi-Arm System for Robotic Horticulture of Sweet Peppers},
author={Christian Lenz and Rohit Menon and Michael Schreiber and Melvin Paul Jacob and Sven Behnke and Maren Bennewitz},
year={2024},
eprint={2403.15306},
archivePrefix={arXiv},
primaryClass={id='cs.RO' full_name='Robotics' is_active=True alt_name=None in_archive='cs' is_general=False description='Roughly includes material in ACM Subject Class I.2.9.'},
url={https://arxiv.org/abs/2403.15306},
doi={10.48550/arXiv.2403.15306}
}

S. Khorshidi, M. Dawood, and M. Bennewitz, Centroidal State Estimation based on the Koopman Embedding for Dynamic Legged Locomotion, 2024. doi:10.48550/arXiv.2403.13366
[BibTeX] [PDF]

@misc{khorshidi2024centroidal,
title={Centroidal State Estimation based on the Koopman Embedding for Dynamic Legged Locomotion},
author={Shahram Khorshidi and Murad Dawood and Maren Bennewitz},
year={2024},
eprint={2403.13366},
archivePrefix={arXiv},
primaryClass={id='cs.RO' full_name='Robotics' is_active=True alt_name=None in_archive='cs' is_general=False description='Roughly includes material in ACM Subject Class I.2.9.'},
url={https://arxiv.org/abs/2403.13366},
doi={10.48550/arXiv.2403.13366}
}

M. T. Kuska, M. Wahabzada, and S. Paulus, "AI for crop production – Where can large language models (LLMs) provide substantial value?," Computers and Electronics in Agriculture, vol. 221, p. 108924, 2024. doi:10.1016/j.compag.2024.108924
[BibTeX] [PDF]

Since the launch of the “Generative Pre-trained Transformer 3.5”, ChatGPT by Open, artificial intelligence (AI) has been a main discussion topic in public. Especially large language models (LLM), so called “intelligent” chatbots, and the possibility to automatically generate highly professional technical texts get high attention. Companies, as well as researchers, are evaluating possible applications and how such a powerful LLM can be integrated into daily work and bring benefits, improve their business or to make the research outcome more efficient. In general, underlying models are trained on large datasets, mainly on sources from websites, and online books and articles. In combination with information provided by the user, the model can give an impressively fast response. Even if the range of questions and answers look unrestricted, there are limits to the models. In this paper, possible use cases for agricultural tasks are elucidated. This includes the textual preparation of facts, consulting tasks, interpretation of decision support models in plant disease management, as well as guides for tutorials to integrate modern digital techniques into agricultural work. Opportunities and challenges are described, as well as limitations and insufficiencies. The authors describe a map of easy-to-reach topics in agriculture where the integration of LLMs seems to be very likely within the next few years.

@article{KUSKA2024108924,
title = {AI for crop production – Where can large language models (LLMs) provide substantial value?},
journal = {Computers and Electronics in Agriculture},
volume = {221},
pages = {108924},
year = {2024},
issn = {0168-1699},
doi = {10.1016/j.compag.2024.108924},
url = {https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4685971},
author = {Matheus Thomas Kuska and Mirwaes Wahabzada and Stefan Paulus},
keywords = {LLM, ChatGPT, AI-assistance, Linguistic editing, Prompt interpretation, Agriculture 4.0, Digital agriculture},
abstract = {Since the launch of the “Generative Pre-trained Transformer 3.5”, ChatGPT by Open, artificial intelligence (AI) has been a main discussion topic in public. Especially large language models (LLM), so called “intelligent” chatbots, and the possibility to automatically generate highly professional technical texts get high attention. Companies, as well as researchers, are evaluating possible applications and how such a powerful LLM can be integrated into daily work and bring benefits, improve their business or to make the research outcome more efficient. In general, underlying models are trained on large datasets, mainly on sources from websites, and online books and articles. In combination with information provided by the user, the model can give an impressively fast response. Even if the range of questions and answers look unrestricted, there are limits to the models. In this paper, possible use cases for agricultural tasks are elucidated. This includes the textual preparation of facts, consulting tasks, interpretation of decision support models in plant disease management, as well as guides for tutorials to integrate modern digital techniques into agricultural work. Opportunities and challenges are described, as well as limitations and insufficiencies. The authors describe a map of easy-to-reach topics in agriculture where the integration of LLMs seems to be very likely within the next few years.}
}

E. Mundschenk, R. Remus, J. Augustin, M. Wissuwa, C. Staudinger, E. Oburger, E. George, and M. Holz, "Fertilizer Addition Modifies Utilization of Different P Sources in Upland Rice on Strongly P-fixing Andosols," Journal of Soil Science and Plant Nutrition, 2024. doi:10.1007/s42729-024-01774-1
[BibTeX] [PDF]

@article{article,
author = {Mundschenk, Eva and Remus, Rainer and Augustin, Jürgen and Wissuwa, Matthias and Staudinger, Christiana and Oburger, Eva and George, Eckhard and Holz, Maire},
year = {2024},
month = {05},
pages = {},
title = {Fertilizer Addition Modifies Utilization of Different P Sources in Upland Rice on Strongly P-fixing Andosols},
journal = {Journal of Soil Science and Plant Nutrition},
doi = {10.1007/s42729-024-01774-1},
URL = {https://link.springer.com/article/10.1007/s42729-024-01774-1},
}

T. S. George, D. Bulgarelli, A. Carminati, Y. Chen, D. Jones, Y. Kuzyakov, A. Schnepf, M. Wissuwa, and T. Roose, "Bottom-up perspective – The role of roots and rhizosphere in climate change adaptation and mitigation in agroecosystems," Plant and Soil, 2024. doi:10.1007/s11104-024-06626-6
[BibTeX] [PDF]

@article{article,
author = {T. S. George and D. Bulgarelli and A. Carminati and Y. Chen and D. Jones and Y. Kuzyakov and A. Schnepf and M. Wissuwa and T. Roose},
year = {2024},
title = {Bottom-up perspective – The role of roots and rhizosphere in climate change adaptation and mitigation in agroecosystems},
journal = {Plant and Soil},
doi = {10.1007/s11104-024-06626-6},
URL = {https://link.springer.com/article/10.1007/s11104-024-06626-6#Abs1},
}

M. El Jarroudi, L. Kouadio, P. Delfosse, C. H. Bock, A. Mahlein, X. Fettweis, F. A. Mercatoris, J. M. Lenne, and S. Hamdioui, "Leveraging edge artificial intelligence for sustainable agriculture," Nature Sustainability, 2024. doi:10.1038/s41893-024-01352-4
[BibTeX] [PDF]

@article{article,
author = {El Jarroudi, Moussa and Kouadio, Louis and Delfosse, Philippe and Bock, Clive H. and Mahlein, Anne-Kathrin and Fettweis, Xavier and Mercatoris, Frank Adams and Lenne, Jillian M. and Hamdioui, Said},
year = {2024},
month = {06},
pages = {},
title = {Leveraging edge artificial intelligence for sustainable agriculture},
journal = {Nature Sustainability},
DOI = {10.1038/s41893-024-01352-4},
URL = {https://www.researchgate.net/publication/372990277_Leveraging_Artificial_Intelligence_for_Enhancing_Agricultural_Productivity_and_Sustainability},
}

A. Ahmadi, M. Halstead, C. Smitt, and C. McCool, BonnBot-I Plus: A Bio-diversity Aware Precise Weed Management Robotic Platform, 2024. doi:10.48550/arXiv.2405.09118
[BibTeX] [PDF]

@misc{ahmadi2024bonnboti,
title={BonnBot-I Plus: A Bio-diversity Aware Precise Weed Management Robotic Platform},
author={Alireza Ahmadi and Michael Halstead and Claus Smitt and Chris McCool},
year={2024},
eprint={2405.09118},
archivePrefix={arXiv},
primaryClass={cs.RO},
DOI = {10.48550/arXiv.2405.09118},
URL = {https://arxiv.org/abs/2405.09118}
}

A. Mahlein, J. G. Arnal Barbedo, K. Chiang, E. M. Del Ponte, and C. H. Bock, "From Detection to Protection: The Role of Optical Sensors, Robots, and Artificial Intelligence in Modern Plant Disease Management," Phytopathology®, iss. ja, p. null, 2024. doi:10.1094/PHYTO-01-24-0009-PER
[BibTeX]

In the past decade, there has been a recognized need for innovative methods to monitor and manage plant diseases aiming to meet the precision demands of modern agriculture. Over the last 15 years, significant advances in detection, monitoring, and management of plant diseases have been made, largely propelled by cutting-edge technologies. Recent advances in precision agriculture have been driven by sophisticated tools such as sensors, artificial intelligence, microsensor networks, and autonomous vehicles. These technologies have enabled the development of novel cropping systems, allowing for targeted management of crops, contrasting with traditional, homogeneous treatment of large crop areas. Research in this field is usually highly collaborative, and interdisciplinary endeavor. It brings together experts from diverse fields such as plant pathology, computer science, statistics, engineering, and agronomy. Despite the progress, translating the advancements in the precision of decision-making or automation into agricultural practice remains a challenge. Enhancing the accuracy and timeliness of disease detection continues to be a priority, with data-driven artificial intelligence systems poised to play a pivotal role. This perspective addresses critical questions and challenges faced in the implementation of digital technologies for plant disease management. It underscores the urgency of integrating innovative technological advances with traditional integrated pest management (IPM). It highlights unresolved issues regarding the establishment of control thresholds for site-specific treatments and the necessary alignment of digital technology use with regulatory frameworks. Importantly, the paper calls for intensified research efforts, widespread knowledge dissemination, and education to optimize the application of digital tools for plant disease management.

@article{doi:10.1094/PHYTO-01-24-0009-PER,
author = {Mahlein, Anne-Katrin and Arnal Barbedo, Jayme G. and Chiang, Kuo-Szu and Del Ponte, Emerson M. and Bock, Clive H.},
title = {From Detection to Protection: The Role of Optical Sensors, Robots, and Artificial Intelligence in Modern Plant Disease Management},
journal = {Phytopathology®},
volume = {0},
number = {ja},
pages = {null},
year = {2024},
doi = {10.1094/PHYTO-01-24-0009-PER},
note ={PMID: 38810274},
abstract = { In the past decade, there has been a recognized need for innovative methods to monitor and manage plant diseases aiming to meet the precision demands of modern agriculture. Over the last 15 years, significant advances in detection, monitoring, and management of plant diseases have been made, largely propelled by cutting-edge technologies. Recent advances in precision agriculture have been driven by sophisticated tools such as sensors, artificial intelligence, microsensor networks, and autonomous vehicles. These technologies have enabled the development of novel cropping systems, allowing for targeted management of crops, contrasting with traditional, homogeneous treatment of large crop areas. Research in this field is usually highly collaborative, and interdisciplinary endeavor. It brings together experts from diverse fields such as plant pathology, computer science, statistics, engineering, and agronomy. Despite the progress, translating the advancements in the precision of decision-making or automation into agricultural practice remains a challenge. Enhancing the accuracy and timeliness of disease detection continues to be a priority, with data-driven artificial intelligence systems poised to play a pivotal role. This perspective addresses critical questions and challenges faced in the implementation of digital technologies for plant disease management. It underscores the urgency of integrating innovative technological advances with traditional integrated pest management (IPM). It highlights unresolved issues regarding the establishment of control thresholds for site-specific treatments and the necessary alignment of digital technology use with regulatory frameworks. Importantly, the paper calls for intensified research efforts, widespread knowledge dissemination, and education to optimize the application of digital tools for plant disease management. }
}

P. Yu, "Seedling root system adaptation to water availability during maize domestication and global expansion," Nature Genetics, 2024. doi:10.1038/s41588-024-01761-3
[BibTeX] [PDF]

@article{article,
author = {Yu, Peng},
year = {2024},
month = {05},
pages = {},
title = {Seedling root system adaptation to water availability during maize domestication and global expansion},
journal = {Nature Genetics},
doi = {10.1038/s41588-024-01761-3},
url = {https://www.researchgate.net/publication/380760842_Seedling_root_system_adaptation_to_water_availability_during_maize_domestication_and_global_expansion},
}

R. H. J. Heim, N. Okole, K. Steppe, M. Van Labeke, I. Geedicke, and W. H. Maes, "An applied framework to unlocking multi-angular UAV reflectance data: a case study for classification of plant parameters in maize (Zea mays)," Precision Agriculture, 2024. doi:10.1007/s11119-024-10133-0
[BibTeX] [PDF]

@ARTICLE{2024PrAgr.tmp...26H,
author = {Heim, Rene H.J. and Okole, Nathan and Steppe, Kathy and Van Labeke, Marie-Christine and Geedicke, Ina and Maes, Wouter H.},
title = "{An applied framework to unlocking multi-angular UAV reflectance data: a case study for classification of plant parameters in maize (Zea mays)}",
journal = {Precision Agriculture},
keywords = {Agriculture, Leaf area index, Machine learning, Phenotyping, View azimuth angle, View zenith angle},
year = 2024,
month = mar,
doi = {10.1007/s11119-024-10133-0},
adsurl = {https://ui.adsabs.harvard.edu/abs/2024PrAgr.tmp...26H},
url = {https://link.springer.com/article/10.1007/s11119-024-10133-0},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

W. Amelung, N. Tang, N. Siebers, M. Aehnelt, K. Eusterhues, V. J. M. N. L. Felde, G. Guggenberger, K. Kaiser, I. Kögel-Knabner, E. Klumpp, C. Knief, J. Kruse, E. Lehndorff, R. Mikutta, S. Peth, N. Ray, A. Prechtel, T. Ritschel, S. A. Schweizer, S. K. Woche, B. Wu, and K. U. Totsche, "Architecture of soil microaggregates: Advanced methodologies to explore properties and functions," Journal of Plant Nutrition and Soil Science, vol. 187, iss. 1, pp. 17-50, 2024. doi:10.1002/jpln.202300149
[BibTeX] [PDF]

Abstract The functions of soils are intimately linked to their three-dimensional pore space and the associated biogeochemical interfaces, mirrored in the complex structure that developed during pedogenesis. Under stress overload, soil disintegrates into smaller compound structures, conventionally named aggregates. Microaggregates (<250 µm) are recognized as the most stable soil structural units. They are built of mineral, organic, and biotic materials, provide habitats for a vast diversity of microorganisms, and are closely involved in the cycling of matter and energy. However, exploring the architecture of soil microaggregates and their linkage to soil functions remains a challenging but demanding scientific endeavor. With the advent of complementary spectromicroscopic and tomographic techniques, we can now assess and visualize the size, composition, and porosity of microaggregates and the spatial arrangement of their interior building units. Their combinations with advanced experimental pedology, multi-isotope labeling experiments, and computational approaches pave the way to investigate microaggregate turnover and stability, explore their role in element cycling, and unravel the intricate linkage between structure and function. However, spectromicroscopic techniques operate at different scales and resolutions, and have specific requirements for sample preparation and microaggregate isolation; hence, special attention must be paid to both the separation of microaggregates in a reproducible manner and the synopsis of the geography of information that originates from the diverse complementary instrumental techniques. The latter calls for further development of strategies for synlocation and synscaling beyond the present state of correlative analysis. Here, we present examples of recent scientific progress and review both options and challenges of the joint application of cutting-edge techniques to achieve a sophisticated picture of the properties and functions of soil microaggregates.

@article{https://doi.org/10.1002/jpln.202300149,
author = {Amelung, Wulf and Tang, Ni and Siebers, Nina and Aehnelt, Michaela and Eusterhues, Karin and Felde, Vincent J. M. N. L. and Guggenberger, Georg and Kaiser, Klaus and Kögel-Knabner, Ingrid and Klumpp, Erwin and Knief, Claudia and Kruse, Jens and Lehndorff, Eva and Mikutta, Robert and Peth, Stephan and Ray, Nadja and Prechtel, Alexander and Ritschel, Thomas and Schweizer, Steffen A. and Woche, Susanne K. and Wu, Bei and Totsche, Kai U.},
title = {Architecture of soil microaggregates: Advanced methodologies to explore properties and functions},
journal = {Journal of Plant Nutrition and Soil Science},
volume = {187},
number = {1},
pages = {17-50},
keywords = {aggregate dispersion and fractionation, in silico soil aggregates, microbial biogeography of aggregates, soil interfaces, spectromicroscopy},
doi = {10.1002/jpln.202300149},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/jpln.202300149},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/jpln.202300149},
abstract = {Abstract The functions of soils are intimately linked to their three-dimensional pore space and the associated biogeochemical interfaces, mirrored in the complex structure that developed during pedogenesis. Under stress overload, soil disintegrates into smaller compound structures, conventionally named aggregates. Microaggregates (<250 µm) are recognized as the most stable soil structural units. They are built of mineral, organic, and biotic materials, provide habitats for a vast diversity of microorganisms, and are closely involved in the cycling of matter and energy. However, exploring the architecture of soil microaggregates and their linkage to soil functions remains a challenging but demanding scientific endeavor. With the advent of complementary spectromicroscopic and tomographic techniques, we can now assess and visualize the size, composition, and porosity of microaggregates and the spatial arrangement of their interior building units. Their combinations with advanced experimental pedology, multi-isotope labeling experiments, and computational approaches pave the way to investigate microaggregate turnover and stability, explore their role in element cycling, and unravel the intricate linkage between structure and function. However, spectromicroscopic techniques operate at different scales and resolutions, and have specific requirements for sample preparation and microaggregate isolation; hence, special attention must be paid to both the separation of microaggregates in a reproducible manner and the synopsis of the geography of information that originates from the diverse complementary instrumental techniques. The latter calls for further development of strategies for synlocation and synscaling beyond the present state of correlative analysis. Here, we present examples of recent scientific progress and review both options and challenges of the joint application of cutting-edge techniques to achieve a sophisticated picture of the properties and functions of soil microaggregates.},
year = {2024}
}

E. Dovydaitis, T. Kunze, F. Born, F. Ewert, S. Dachbrodt-Saaydeh, and K. Grahmann, "Assessing pollen beetle dynamics in diversified agricultural landscapes with reduced pesticide management strategies," , pp. 1-24, 2024. doi:10.5073/LBF.2023.01.03
[BibTeX] [PDF]

@article{article,
author = {Dovydaitis, Emily and Kunze, Thomas and Born, Fabian and Ewert, Frank and Dachbrodt-Saaydeh, Silke and Grahmann, Kathrin},
year = {2024},
month = {01},
pages = {1-24},
title = {Assessing pollen beetle dynamics in diversified agricultural landscapes with reduced pesticide management strategies},
doi = {10.5073/LBF.2023.01.03},
url={https://ojs.openagrar.de/index.php/LBF/article/view/17239/16849}
}

F. R. Ispizua Yamati, M. Günder, A. Barreto, J. Bömer, D. Laufer, C. Bauckhage, and A. Mahlein, "Automatic Scoring of Rhizoctonia Crown and Root Rot Affected Sugar Beet Fields from Orthorectified UAV Images Using Machine Learning," Plant Disease, vol. 108, iss. 3, pp. 711-724, 2024. doi:10.1094/PDIS-04-23-0779-RE
[BibTeX]

Rhizoctonia crown and root rot (RCRR), caused by Rhizoctonia solani, can cause severe yield and quality losses in sugar beet. The most common strategy to control the disease is the development of resistant varieties. In the breeding process, field experiments with artificial inoculation are carried out to evaluate the performance of genotypes and varieties. The phenotyping process in breeding trials requires constant monitoring and scoring by skilled experts. This work is time demanding and shows bias and heterogeneity according to the experience and capacity of each individual person. Optical sensors and artificial intelligence have demonstrated great potential to achieve higher accuracy than human raters and the possibility to standardize phenotyping applications. A workflow combining red–green–blue and multispectral imagery coupled to an unmanned aerial vehicle (UAV), as well as machine learning techniques, was applied to score diseased plants and plots affected by RCRR. Georeferenced annotation of UAV-orthorectified images was carried out. With the annotated images, five convolutional neural networks were trained to score individual plants. The training was carried out with different image analysis strategies and data augmentation. The custom convolutional neural network trained from scratch together with pretrained MobileNet showed the best precision in scoring RCRR (0.73 to 0.85). The average per plot of spectral information was used to score the plots, and the benefit of adding the information obtained from the score of individual plants was compared. For this purpose, machine learning models were trained together with data management strategies, and the best-performing model was chosen. A combined pipeline of random forest and k-nearest neighbors has shown the best weighted precision (0.67). This research provides a reliable workflow for detecting and scoring RCRR based on aerial imagery. RCRR is often distributed heterogeneously in trial plots; therefore, considering the information from individual plants of the plots showed a significant improvement in UAV-based automated monitoring routines.

@article{doi:10.1094/PDIS-04-23-0779-RE,
author = {Ispizua Yamati, Facundo Ram\'{o}n and G\"{u}nder, Maurice and Barreto, Abel and B\"{o}mer, Jonas and Laufer, Daniel and Bauckhage, Christian and Mahlein, Anne-Katrin},
title = {Automatic Scoring of Rhizoctonia Crown and Root Rot Affected Sugar Beet Fields from Orthorectified UAV Images Using Machine Learning},
journal = {Plant Disease},
volume = {108},
number = {3},
pages = {711-724},
year = {2024},
doi = {10.1094/PDIS-04-23-0779-RE},
note ={PMID: 37755420},
abstract = { Rhizoctonia crown and root rot (RCRR), caused by Rhizoctonia solani, can cause severe yield and quality losses in sugar beet. The most common strategy to control the disease is the development of resistant varieties. In the breeding process, field experiments with artificial inoculation are carried out to evaluate the performance of genotypes and varieties. The phenotyping process in breeding trials requires constant monitoring and scoring by skilled experts. This work is time demanding and shows bias and heterogeneity according to the experience and capacity of each individual person. Optical sensors and artificial intelligence have demonstrated great potential to achieve higher accuracy than human raters and the possibility to standardize phenotyping applications. A workflow combining red–green–blue and multispectral imagery coupled to an unmanned aerial vehicle (UAV), as well as machine learning techniques, was applied to score diseased plants and plots affected by RCRR. Georeferenced annotation of UAV-orthorectified images was carried out. With the annotated images, five convolutional neural networks were trained to score individual plants. The training was carried out with different image analysis strategies and data augmentation. The custom convolutional neural network trained from scratch together with pretrained MobileNet showed the best precision in scoring RCRR (0.73 to 0.85). The average per plot of spectral information was used to score the plots, and the benefit of adding the information obtained from the score of individual plants was compared. For this purpose, machine learning models were trained together with data management strategies, and the best-performing model was chosen. A combined pipeline of random forest and k-nearest neighbors has shown the best weighted precision (0.67). This research provides a reliable workflow for detecting and scoring RCRR based on aerial imagery. RCRR is often distributed heterogeneously in trial plots; therefore, considering the information from individual plants of the plots showed a significant improvement in UAV-based automated monitoring routines. }
}

Y. Kim, H. Webber, S. G. K. Adiku, R. S. de Nóia Júnior, J. Deswarte, S. Asseng, and F. Ewert, "Mechanisms and modelling approaches for excessive rainfall stress on cereals: Waterlogging, submergence, lodging, pests and diseases," Agricultural and Forest Meteorology, vol. 344, p. 109819, 2024. doi:10.1016/j.agrformet.2023.109819
[BibTeX] [PDF]

As the intensity and frequency of extreme weather events are projected to increase under climate change, assessing their impact on cropping systems and exploring feasible adaptation options is increasingly critical. Process-based crop models (PBCMs), which are widely used in climate change impact assessments, have improved in simulating the impacts of major extreme weather events such as heatwaves and droughts but still fail to reproduce low crop yields under wet conditions. Here, we provide an overview of yield-loss mechanisms of excessive rainfall in cereals (i.e., waterlogging, submergence, lodging, pests and diseases) and associated modelling approaches with the aim of guiding PBCM improvements. Some PBCMs simulate waterlogging and ponding environments, but few capture aeration stresses on crop growth. Lodging is often neglected by PBCMs; however, some stand-alone mechanistic lodging models exist, which can potentially be incorporated into PBCMs. Some frameworks link process-based epidemic and crop models with consideration of different damage mechanisms. However, the lack of data to calibrate and evaluate these model functions limit the use of such frameworks. In order to generate data for model improvement and close knowledge gaps, targeted experiments on damage mechanisms of waterlogging, submergence, pests and diseases are required. However, consideration of all damage mechanisms in PBCM may result in excessively complex models with a large number of parameters, increasing model uncertainty. Modular frameworks could assist in selecting necessary mechanisms and lead to appropriate model structures and complexity that fit a specific research question. Lastly, there are potential synergies between PBCMs, statistical models, and remotely sensed data that could improve the prediction accuracy and understanding of current PBCMs' shortcomings.

@article{KIM2024109819,
title = {Mechanisms and modelling approaches for excessive rainfall stress on cereals: Waterlogging, submergence, lodging, pests and diseases},
journal = {Agricultural and Forest Meteorology},
volume = {344},
pages = {109819},
year = {2024},
issn = {0168-1923},
doi = {10.1016/j.agrformet.2023.109819},
url = {https://publications.zalf.de/publications/8D4FF46B-AFBB-425D-99B9-32E21A398015-1.pdf},
author = {Yean-Uk Kim and Heidi Webber and Samuel G.K. Adiku and Rogério de S. {Nóia Júnior} and Jean-Charles Deswarte and Senthold Asseng and Frank Ewert},
keywords = {Excess rain, Yield loss mechanisms, Process-based crop model, Model improvement},
abstract = {As the intensity and frequency of extreme weather events are projected to increase under climate change, assessing their impact on cropping systems and exploring feasible adaptation options is increasingly critical. Process-based crop models (PBCMs), which are widely used in climate change impact assessments, have improved in simulating the impacts of major extreme weather events such as heatwaves and droughts but still fail to reproduce low crop yields under wet conditions. Here, we provide an overview of yield-loss mechanisms of excessive rainfall in cereals (i.e., waterlogging, submergence, lodging, pests and diseases) and associated modelling approaches with the aim of guiding PBCM improvements. Some PBCMs simulate waterlogging and ponding environments, but few capture aeration stresses on crop growth. Lodging is often neglected by PBCMs; however, some stand-alone mechanistic lodging models exist, which can potentially be incorporated into PBCMs. Some frameworks link process-based epidemic and crop models with consideration of different damage mechanisms. However, the lack of data to calibrate and evaluate these model functions limit the use of such frameworks. In order to generate data for model improvement and close knowledge gaps, targeted experiments on damage mechanisms of waterlogging, submergence, pests and diseases are required. However, consideration of all damage mechanisms in PBCM may result in excessively complex models with a large number of parameters, increasing model uncertainty. Modular frameworks could assist in selecting necessary mechanisms and lead to appropriate model structures and complexity that fit a specific research question. Lastly, there are potential synergies between PBCMs, statistical models, and remotely sensed data that could improve the prediction accuracy and understanding of current PBCMs' shortcomings.}
}

J. Weyler, T. Läbe, J. Behley, and C. Stachniss, "Panoptic Segmentation with Partial Annotations for Agricultural Robots," ral, vol. 9, iss. 2, pp. 1660-1667, 2024. doi:10.1109/LRA.2023.3346760
[BibTeX] [PDF] [Code]

@article{weyler2024ral,
author = {J. Weyler and T. L\"abe and J. Behley and C. Stachniss},
title = {{Panoptic Segmentation with Partial Annotations for Agricultural Robots}},
journal = ral,
year = {2024},
volume = {9},
number = {2},
pages = {1660-1667},
issn = {2377-3766},
doi = {10.1109/LRA.2023.3346760},
codeurl = {https://github.com/PRBonn/MapClosures},
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/weyler2024ral.pdf},
}

S. Gupta, T. Guadagnino, B. Mersch, I. Vizzo, and C. Stachniss, "Effectively Detecting Loop Closures using Point Cloud Density Maps," in Proc.~of the IEEE Intl.~Conf.~on Robotics & Automation (ICRA) , 2024.
[BibTeX] [PDF] [Code] [Video]

@inproceedings{gupta2024icra,
author = {S. Gupta and T. Guadagnino and B. Mersch and I. Vizzo and C. Stachniss},
title = {{Effectively Detecting Loop Closures using Point Cloud Density Maps}},
booktitle = {Proc.~of the IEEE Intl.~Conf.~on Robotics \& Automation (ICRA)},
year = 2024,
codeurl = {https://github.com/PRBonn/MapClosures},
videourl = {https://youtu.be/BpwR_aLXrNo},
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/gupta2024icra.pdf}
}

F. Magistri, R. Marcuzzi, E. A. Marks, M. Sodano, J. Behley, and C. Stachniss, "Efficient and Accurate Transformer-Based 3D Shape Completion and Reconstruction of Fruits for Agricultural Robots," in Proc.~of the IEEE Intl.~Conf.~on Robotics & Automation (ICRA) , 2024.
[BibTeX] [PDF] [Code] [Video]

@inproceedings{magistri2024icra,
author = {F. Magistri and R. Marcuzzi and E.A. Marks and M. Sodano and J. Behley and C. Stachniss},
title = {{Efficient and Accurate Transformer-Based 3D Shape Completion and Reconstruction of Fruits for Agricultural Robots}},
booktitle = {Proc.~of the IEEE Intl.~Conf.~on Robotics \& Automation (ICRA)},
year = 2024,
videourl = {https://youtu.be/U1xxnUGrVL4},
codeurl = {https://github.com/PRBonn/TCoRe},
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/magistri2024icra.pdf}
}

L. Nunes, R. Marcuzzi, B. Mersch, J. Behley, and C. Stachniss, "Scaling Diffusion Models to Real-World 3D LiDAR Scene Completion," in Proc.~of the IEEE/CVF Conf.~on Computer Vision and Pattern Recognition (CVPR) , 2024.
[BibTeX] [PDF] [Code] [Video]

@inproceedings{nunes2024cvpr,
author = {L. Nunes and R. Marcuzzi and B. Mersch and J. Behley and C. Stachniss},
title = {{Scaling Diffusion Models to Real-World 3D LiDAR Scene Completion}},
booktitle = {Proc.~of the IEEE/CVF Conf.~on Computer Vision and Pattern Recognition (CVPR)},
year = 2024,
codeurl = {https://github.com/PRBonn/LiDiff},
videourl = {https://www.youtube.com/watch?v=K7BpT_cltio
},
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/nunes2024cvpr.pdf}
}

M. Sodano, F. Magistri, L. Nunes, J. Behley, and C. Stachniss, "Open-World Semantic Segmentation Including Class Similarity," in Proc.~of the IEEE/CVF Conf.~on Computer Vision and Pattern Recognition (CVPR) , 2024.
[BibTeX] [PDF] [Code] [Video]

@inproceedings{sodano2024cvpr,
author = {M. Sodano and F. Magistri and L. Nunes and J. Behley and C. Stachniss},
title = {{Open-World Semantic Segmentation Including Class Similarity}},
booktitle = {Proc.~of the IEEE/CVF Conf.~on Computer Vision and Pattern Recognition (CVPR)},
year = 2024,
codeurl = {https://github.com/PRBonn/ContMAV},
videourl = {https://www.youtube.com/watch?v=ei2cbyPQgag},
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/sodano2024cvpr.pdf}
}

M. H. Shams Eddin and J. Gall, "Focal-TSMP: deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation," Geoscientific Model Development, vol. 17, iss. 7, p. 2987–3023, 2024. doi:10.5194/gmd-17-2987-2024
[BibTeX] [PDF] [Code] [Video]

@Article{gmd-17-2987-2024,
AUTHOR = {Shams Eddin, M. H. and Gall, J.},
TITLE = {Focal-TSMP: deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation},
JOURNAL = {Geoscientific Model Development},
VOLUME = {17},
YEAR = {2024},
NUMBER = {7},
PAGES = {2987--3023},
CODEURL = {https://github.com/HakamShams/Focal_TSMP},
VIDEOURL = {https://www.youtube.com/watch?v=7m-85sDGwe8},
URL = {https://gmd.copernicus.org/articles/17/2987/2024/},
DOI = {10.5194/gmd-17-2987-2024}
}

I. M. Hernández-Ochoa, T. Gaiser, K. Grahmann, A. Engels, K. Kersebaum, S. J. Seidel, and F. Ewert, "Cross model validation for a diversified cropping system," European Journal of Agronomy, vol. 157, p. 127181, 2024. doi:10.1016/j.eja.2024.127181
[BibTeX] [PDF]

Crop diversification is gaining traction due to the positive benefits in the delivery of ecosystem services (ESS) and the promotion of biodiversity. Agroecosystem simulation models can contribute to the design of diversified cropping systems but require calibration and validation before they can be applied. However, data availability is still very limited, particularly for diversified cropping systems. Therefore, the main goal of this study was to evaluate the suitability of the Nelder-Mead optimization method and the leave-one-out (LOO) validation method to calibrate and validate a diversified cropping system with a limited dataset, by using either a fixed year combination for calibration and validation for all crops or using a flexible year combination for every crop. Crop phenology was manually calibrated for all year combinations and the best parameter set based on the LOO-validation was selected for the subsequent step. Next, a four-parameter set related to crop growth and biomass dynamics was chosen for parameter optimization in the calibration step. To measure model performance during both steps, the root mean square error (RMSE) in days was used for phenology and a weighed relative RMSE (RRMSE) was used for crop growth, with the intermediate and final biomass contributing to 50% of the error and the other 50% corresponding to grain yield. Data for model comparison was collected at the patchCROP landscape experiment in Brandenburg, Germany. Observed data included daily weather, soil information, crop phenology, intermediate and final above ground biomass and grain yield for summer seasons 2020, 2021, and 2022 and winter seasons 2020/2021 and 2021/2022 (referred as 2021 and 2022, respectively). Summer crops included maize, soybean, lupine and sunflower, while winter crops were wheat, barley, rye and rapeseed. Results showed that the Nelder-Mead method was successful in reducing the error between observed and simulated data. As for the LOO-validation, the method showed that different year combinations led to a similar RMSE for phenology. However, for crop growth, optimum year combination was critical, as it differed for all summer crops but not for winter crops. For the summer crops, the lowest errors in the LOO-validation were observed in lupine, maize and soybean, with <20.6% RRMSE, while sunflower resulted in a reasonable LOO-validated value with 31.2% RRMSE, but a poor performance in the calibration step with 68.7% RRMSE. For the winter crops, the 2022 calibration year and the 2021 validation year combination resulted in the lowest RRMSE for wheat, barley and rapeseed. However, for rye, both year combinations led to a large error, with the lowest error when using the 2021 season for calibration (65.9% RRMSE) and 2022 season for validation (33.0% RRMSE). The flexible LOO-validation method was useful to make optimal use of the limited dataset as it allowed a more through model testing and pointed to differences among summer and winter crops. The newly validated model has the potential to be used for the design of diversified cropping systems.

@article{HERNANDEZOCHOA2024127181,
title = {Cross model validation for a diversified cropping system},
journal = {European Journal of Agronomy},
volume = {157},
pages = {127181},
year = {2024},
issn = {1161-0301},
doi = {10.1016/j.eja.2024.127181},
url = {https://publications.zalf.de/publications/EFCA2660-69C4-4CCA-9CCD-6BFE941B644B-1.pdf},
author = {Ixchel M. Hernández-Ochoa and Thomas Gaiser and Kathrin Grahmann and Anna Engels and Kurt-Christian Kersebaum and Sabine J. Seidel and Frank Ewert},
keywords = {Nelder-Mead method, LOO-validation, Process-based crop model, Crop model, Model calibration},
abstract = {Crop diversification is gaining traction due to the positive benefits in the delivery of ecosystem services (ESS) and the promotion of biodiversity. Agroecosystem simulation models can contribute to the design of diversified cropping systems but require calibration and validation before they can be applied. However, data availability is still very limited, particularly for diversified cropping systems. Therefore, the main goal of this study was to evaluate the suitability of the Nelder-Mead optimization method and the leave-one-out (LOO) validation method to calibrate and validate a diversified cropping system with a limited dataset, by using either a fixed year combination for calibration and validation for all crops or using a flexible year combination for every crop. Crop phenology was manually calibrated for all year combinations and the best parameter set based on the LOO-validation was selected for the subsequent step. Next, a four-parameter set related to crop growth and biomass dynamics was chosen for parameter optimization in the calibration step. To measure model performance during both steps, the root mean square error (RMSE) in days was used for phenology and a weighed relative RMSE (RRMSE) was used for crop growth, with the intermediate and final biomass contributing to 50% of the error and the other 50% corresponding to grain yield. Data for model comparison was collected at the patchCROP landscape experiment in Brandenburg, Germany. Observed data included daily weather, soil information, crop phenology, intermediate and final above ground biomass and grain yield for summer seasons 2020, 2021, and 2022 and winter seasons 2020/2021 and 2021/2022 (referred as 2021 and 2022, respectively). Summer crops included maize, soybean, lupine and sunflower, while winter crops were wheat, barley, rye and rapeseed. Results showed that the Nelder-Mead method was successful in reducing the error between observed and simulated data. As for the LOO-validation, the method showed that different year combinations led to a similar RMSE for phenology. However, for crop growth, optimum year combination was critical, as it differed for all summer crops but not for winter crops. For the summer crops, the lowest errors in the LOO-validation were observed in lupine, maize and soybean, with <20.6% RRMSE, while sunflower resulted in a reasonable LOO-validated value with 31.2% RRMSE, but a poor performance in the calibration step with 68.7% RRMSE. For the winter crops, the 2022 calibration year and the 2021 validation year combination resulted in the lowest RRMSE for wheat, barley and rapeseed. However, for rye, both year combinations led to a large error, with the lowest error when using the 2021 season for calibration (65.9% RRMSE) and 2022 season for validation (33.0% RRMSE). The flexible LOO-validation method was useful to make optimal use of the limited dataset as it allowed a more through model testing and pointed to differences among summer and winter crops. The newly validated model has the potential to be used for the design of diversified cropping systems.}
}

K. Grahmann, M. Reckling, I. Hernández-Ochoa, M. Donat, S. Bellingrath-Kimura, and F. Ewert, "Co-designing a landscape experiment to investigate diversified cropping systems," Agricultural Systems, vol. 217, p. 103950, 2024. doi:10.1016/j.agsy.2024.103950
[BibTeX] [PDF]

CONTEXT Intensive food and feed production in sole-cropped, large fields with high fertilizer and pesticide inputs to achieve high yields, has contributed to detrimental environmental impacts. To move towards more sustainable agricultural landscapes, cropping system diversification has been suggested as a promising practice for which the use of digital technologies could be potentially beneficial. Understanding the impact of diversified, newly arranged cropping systems and their management requires long-term experimental data at the landscape scale and practical experiences in using digital technologies which are hardly available. Experimental platforms in an agricultural landscape setup with farmers' involvement could meet such demands but have not been set up in many regions nor has the process of designing such platforms been described systematically. OBJECTIVE The overall objective of this study was to describe how an experimental platform can be co-designed jointly by researchers and practitioners to study and understand the impact of diversification practices compared to current cropping systems in Eastern Brandenburg, Germany. Specifically, we aimed to re-design an intensively managed field into smaller field segments that we called patches and to assess the potential of a co-created landscape experiment for sustainable agricultural production focussing on both, the practitioners´ and scientists´ perspective. METHODS We used the DEED research cycle (Describe, Explain, Explore and Design) as a conceptual framework to co-design the landscape experiment called patchCROP within a commercial farm. Patches were implemented as 0.5 ha fields within the original field based on yield and soil maps using advanced cluster analysis which considered soil heterogeneity. The original narrow crop sequence was diversified by integrating new crops, cover crops and flower strips for a five-year crop rotation. To cultivate the patches, large machinery was used during the first years but will be replaced over time with autonomous field robots. Workshops and various methods such as a SWOT analysis were used to adjust the management practices towards pesticide reduction. RESULTS AND CONCLUSIONS The SWOT analysis revealed opportunities and drawbacks to develop such a research platform in a participative manner from both the scientific and practical farming perspective. We found that the farmer-centric position focused mainly on the economic return and feasibility of future field operations in the diversified field. The scientific perspective on the other hand described needs and potentials about the research process for evaluating dynamic, interdependent or opposing natural processes and their interactions like productivity, biodiversity and ecosystem service changes in an agricultural landscape context. SIGNIFICANCE Co-designed landscape experiments have the potential to simultaneously assess the impact of newly developed cropping systems on biodiversity and ecosystem services beyond the field level, crop performance and soil quality at multiple scales, and the implications for multiple actors. This is a step forward to extend systems-based research from single plot to landscape research in an on-farm environment, allowing the exploration of diversification measures with new digital technologies in the long run.

@article{GRAHMANN2024103950,
title = {Co-designing a landscape experiment to investigate diversified cropping systems},
journal = {Agricultural Systems},
volume = {217},
pages = {103950},
year = {2024},
issn = {0308-521X},
doi = {10.1016/j.agsy.2024.103950},
url = {https://publications.zalf.de/publications/04497CDF-4E3C-4492-9911-08A8E9781B2E-1.pdf},
author = {Kathrin Grahmann and Moritz Reckling and Ixchel Hernández-Ochoa and Marco Donat and Sonoko Bellingrath-Kimura and Frank Ewert},
keywords = {Biodiversity, Ecosystem services, DEED, Digitalization, On-farm experimentation, Soil heterogeneity},
abstract = {CONTEXT
Intensive food and feed production in sole-cropped, large fields with high fertilizer and pesticide inputs to achieve high yields, has contributed to detrimental environmental impacts. To move towards more sustainable agricultural landscapes, cropping system diversification has been suggested as a promising practice for which the use of digital technologies could be potentially beneficial. Understanding the impact of diversified, newly arranged cropping systems and their management requires long-term experimental data at the landscape scale and practical experiences in using digital technologies which are hardly available. Experimental platforms in an agricultural landscape setup with farmers' involvement could meet such demands but have not been set up in many regions nor has the process of designing such platforms been described systematically.
OBJECTIVE
The overall objective of this study was to describe how an experimental platform can be co-designed jointly by researchers and practitioners to study and understand the impact of diversification practices compared to current cropping systems in Eastern Brandenburg, Germany. Specifically, we aimed to re-design an intensively managed field into smaller field segments that we called patches and to assess the potential of a co-created landscape experiment for sustainable agricultural production focussing on both, the practitioners´ and scientists´ perspective.
METHODS
We used the DEED research cycle (Describe, Explain, Explore and Design) as a conceptual framework to co-design the landscape experiment called patchCROP within a commercial farm. Patches were implemented as 0.5 ha fields within the original field based on yield and soil maps using advanced cluster analysis which considered soil heterogeneity. The original narrow crop sequence was diversified by integrating new crops, cover crops and flower strips for a five-year crop rotation. To cultivate the patches, large machinery was used during the first years but will be replaced over time with autonomous field robots. Workshops and various methods such as a SWOT analysis were used to adjust the management practices towards pesticide reduction.
RESULTS AND CONCLUSIONS
The SWOT analysis revealed opportunities and drawbacks to develop such a research platform in a participative manner from both the scientific and practical farming perspective. We found that the farmer-centric position focused mainly on the economic return and feasibility of future field operations in the diversified field. The scientific perspective on the other hand described needs and potentials about the research process for evaluating dynamic, interdependent or opposing natural processes and their interactions like productivity, biodiversity and ecosystem service changes in an agricultural landscape context.
SIGNIFICANCE
Co-designed landscape experiments have the potential to simultaneously assess the impact of newly developed cropping systems on biodiversity and ecosystem services beyond the field level, crop performance and soil quality at multiple scales, and the implications for multiple actors. This is a step forward to extend systems-based research from single plot to landscape research in an on-farm environment, allowing the exploration of diversification measures with new digital technologies in the long run.}
}

E. S. T. Bacud, M. K. Gerullis, R. Puskur, and T. Heckelei, "Looking at gender is not enough—How diversity of farmers’ marginalization relates to varietal trait preferences," Food Policy, vol. 124, p. 102616, 2024. doi:10.1016/j.foodpol.2024.102616
[BibTeX] [PDF]

Improved crop varieties help farmers adapt to changing climate and socioeconomic challenges. They are essential for meeting the global food demand, but their adoption remains slow and low. One reason for this unsuccessful adoption is the disregard of trait preferences and marginalized contexts of diverse users by actors in varietal development and delivery. The general wisdom regarding trait preferences includes gender-distinct priorities, in which men focus on high yield and marketability, while women prefer good taste and other cooking attributes. However, although gender is a first step toward nuanced preferences, most analyses restrict themselves to gender-based comparisons (frequently using the sex of heads of households), which homogenizes socioeconomic conditions and preferences within gender. Using intrahousehold preference data, our study reveals that the intersection between gender and other social categories presents compounded marginalization that corresponds to similarities or differences in women’s and men’s trait preferences. Cluster analysis reveals that trait preferences of women and men overlap but differ in the traits’ relative importance. Trait preferences are comparable in low-wealth clusters as they operate in similar marginalized contexts and diverge in high-wealth clusters. Furthermore, logit regression shows that factors of marginalization, gender roles, and agency are associated with increased odds of prioritizing specific traits, such as market and culinary traits. Our results demonstrate how diversity of marginalization and intersectionality matters more than gender dichotomies. We anticipate that our intersectional approach to understanding gendered trait preferences can enhance targeted, demand-led, and inclusive varietal development and delivery in the future.

@article{BACUD2024102616,
title = {Looking at gender is not enough—How diversity of farmers’ marginalization relates to varietal trait preferences},
journal = {Food Policy},
volume = {124},
pages = {102616},
year = {2024},
issn = {0306-9192},
doi = {10.1016/j.foodpol.2024.102616},
url = {https://www.sciencedirect.com/science/article/pii/S0306919224000277?via%3Dihub},
author = {Eva Salve Tino Bacud and Maria Katharina Gerullis and Ranjitha Puskur and Thomas Heckelei},
keywords = {Farmer preference, Technology adoption, Gender, Intersectionality, Marginalization, Multivariate analysis},
abstract = {Improved crop varieties help farmers adapt to changing climate and socioeconomic challenges. They are essential for meeting the global food demand, but their adoption remains slow and low. One reason for this unsuccessful adoption is the disregard of trait preferences and marginalized contexts of diverse users by actors in varietal development and delivery. The general wisdom regarding trait preferences includes gender-distinct priorities, in which men focus on high yield and marketability, while women prefer good taste and other cooking attributes. However, although gender is a first step toward nuanced preferences, most analyses restrict themselves to gender-based comparisons (frequently using the sex of heads of households), which homogenizes socioeconomic conditions and preferences within gender. Using intrahousehold preference data, our study reveals that the intersection between gender and other social categories presents compounded marginalization that corresponds to similarities or differences in women’s and men’s trait preferences. Cluster analysis reveals that trait preferences of women and men overlap but differ in the traits’ relative importance. Trait preferences are comparable in low-wealth clusters as they operate in similar marginalized contexts and diverge in high-wealth clusters. Furthermore, logit regression shows that factors of marginalization, gender roles, and agency are associated with increased odds of prioritizing specific traits, such as market and culinary traits. Our results demonstrate how diversity of marginalization and intersectionality matters more than gender dichotomies. We anticipate that our intersectional approach to understanding gendered trait preferences can enhance targeted, demand-led, and inclusive varietal development and delivery in the future.}
}

J. Hase, M. Weigand, and A. Kemna, "A probabilistic solution to geophysical inverse problems in complex variables and its application to complex resistivity imaging," Geophysical Journal International, vol. 237, iss. 1, pp. 456-464, 2024. doi:10.1093/gji/ggae045
[BibTeX] [PDF]

{We introduce a novel probabilistic framework for the solution of non-linear geophysical inverse problems in complex variables. By using complex probability distributions, this approach can simultaneously account for individual errors of real and imaginary data parts, independently regularize real and imaginary parts of the complex model, and still take into account cross-sensitivities resulting from a complex forward calculation. The inverse problem is solved by means of optimization. An application of the framework to complex resistivity (CR) imaging demonstrates its advantages over the established inversion approach for CR measurements. We show that CR data, with real and imaginary parts being subject to different errors, can be fitted adequately, accounting for the individual errors and applying independent regularization to the real and imaginary part of the subsurface conductivity. The probabilistic framework itself serves as a basis for the future application of global sampling approaches, such as Markov chain Monte Carlo methods.}

@article{10.1093/gji/ggae045,
author = {Hase, Joost and Weigand, Maximilian and Kemna, Andreas},
title = "{A probabilistic solution to geophysical inverse problems in complex variables and its application to complex resistivity imaging}",
journal = {Geophysical Journal International},
volume = {237},
number = {1},
pages = {456-464},
year = {2024},
month = {02},
abstract = "{We introduce a novel probabilistic framework for the solution of non-linear geophysical inverse problems in complex variables. By using complex probability distributions, this approach can simultaneously account for individual errors of real and imaginary data parts, independently regularize real and imaginary parts of the complex model, and still take into account cross-sensitivities resulting from a complex forward calculation. The inverse problem is solved by means of optimization. An application of the framework to complex resistivity (CR) imaging demonstrates its advantages over the established inversion approach for CR measurements. We show that CR data, with real and imaginary parts being subject to different errors, can be fitted adequately, accounting for the individual errors and applying independent regularization to the real and imaginary part of the subsurface conductivity. The probabilistic framework itself serves as a basis for the future application of global sampling approaches, such as Markov chain Monte Carlo methods.}",
issn = {0956-540X},
doi = {10.1093/gji/ggae045},
url = {https://academic.oup.com/gji/article-pdf/237/1/456/56773179/ggae045.pdf},
eprint = {https://academic.oup.com/gji/article-pdf/237/1/456/56773179/ggae045.pdf},
}

S. Paulus and D. Koops, "Robotics for weed control – a classification of the potential and the market," Sugar Industry International, vol. 149, pp. 208-211, 2024. doi:10.36961/si31015
[BibTeX]

@article{paulus2024sugarindustry,
author = {Paulus, Stefan AND Koops, Dirk},
title = {{Robotics for weed control – a classification of the potential and the market}},
journal = {Sugar Industry International},
volume = {149},
issue = {3},
year = {2024},
doi = {10.36961/si31015},
pages = {208-211},
videourl = {},
keywords = {Spot application, Automated weeding, AI based decision support}
abstract = “{Agriculture is undergoing a significant change, with robots playing an increasingly important role. Among these robots, weeding robots are particularly helpful in automating the weeding process on fields and have recently been introduced to the market. These robots operate using a GNSS (Global navigation satellite system) or vision-based approach, which is explained and evaluated in this context. In contrast to the relatively easy method of storing the crop position in the GNSS-based process, vision-based approaches require a sophisticated image analysis, usually based on deep learning routines and extensive sets of training data. This concept has been successfully applied to two market-ready robots, and a market overview following the introduced classification is also provided. The paper concludes with a discussion of the challenges involved in using robots in the field and how these robots can support existing agriculture workflows and interaction models between remote sensing and field robots.}”,
}

B. Kamali, S. H. Ahmadi, T. Gaiser, M. Buddeberg, and C. Nendel, "Quest to find compromised spatial and temporal resolutions for integrating remote sensing data with an agro-ecosystem model for grasslands," International Journal of Applied Earth Observation and Geoinformation, vol. 128, p. 103705, 2024. doi:10.1016/j.jag.2024.103705
[BibTeX] [PDF]

This paper addressed one of the main challenges in assimilating remote sensing derived variables into process-based crop model simulations, which is the inconsistent spatial and temporal resolution between information obtained from remote sensing and the outputs of process-based agroecosystem model. We proposed an applied method to reduce the number of required simulations by identifying (i) optimal points in time where additional information from remote sensing has the largest positive influence on the model performance and (ii) options to cluster 10 m grid cells to larger cells without compromising their information content. The MONICA (Model for Nitrogen and Carbon) model was applied to simulate above and below ground biomass in two grassland sites located in southern and eastern parts of Germany. The model was calibrated using LAI values obtained from Sentinel-2 and the sensitivity of output variables to two key root parameters (Root Form factor and specific root length) was evaluated. Our results showed that one or two satellite images covering the critical time periods right after cutting events significantly improved the predictions of grass yields produced by a mechanistic agroecosystem models (by up to 30 %). A larger number of images at other grass growth stages would not further improve the predictive power of the model. We also found that the sensitivity to these critical time periods was independent of model parameters. The mixed-resolution scheme (between 10 and 50 m) achieved better results compared with the high-resolution standalone state-updating method, yet it reduced computational costs by more than 50 %. In conclusion, we proposed a methodology to reduce the number of required simulations for data assimilation by aggregating data from fine to coarse resolutions. Our method was promising for applying data assimilation over large areas and benefiting more from satellite information for real-time prediction of agricultural productivity.

@article{KAMALI2024103705,
title = {Quest to find compromised spatial and temporal resolutions for integrating remote sensing data with an agro-ecosystem model for grasslands},
journal = {International Journal of Applied Earth Observation and Geoinformation},
volume = {128},
pages = {103705},
year = {2024},
issn = {1569-8432},
doi = {10.1016/j.jag.2024.103705},
url = {https://www.sciencedirect.com/science/article/pii/S1569843224000591},
author = {Bahareh Kamali and Seyed Hamid Ahmadi and Thomas Gaiser and Marion Buddeberg and Claas Nendel},
keywords = {Grasslands, Sentinel-2, Root-to-shoot ratio, Above ground biomass, Leaf area index, Agro-ecosystem modeling, Below ground biomass, Data assimilation, Spatial resolution},
abstract = {This paper addressed one of the main challenges in assimilating remote sensing derived variables into process-based crop model simulations, which is the inconsistent spatial and temporal resolution between information obtained from remote sensing and the outputs of process-based agroecosystem model. We proposed an applied method to reduce the number of required simulations by identifying (i) optimal points in time where additional information from remote sensing has the largest positive influence on the model performance and (ii) options to cluster 10 m grid cells to larger cells without compromising their information content. The MONICA (Model for Nitrogen and Carbon) model was applied to simulate above and below ground biomass in two grassland sites located in southern and eastern parts of Germany. The model was calibrated using LAI values obtained from Sentinel-2 and the sensitivity of output variables to two key root parameters (Root Form factor and specific root length) was evaluated. Our results showed that one or two satellite images covering the critical time periods right after cutting events significantly improved the predictions of grass yields produced by a mechanistic agroecosystem models (by up to 30 %). A larger number of images at other grass growth stages would not further improve the predictive power of the model. We also found that the sensitivity to these critical time periods was independent of model parameters. The mixed-resolution scheme (between 10 and 50 m) achieved better results compared with the high-resolution standalone state-updating method, yet it reduced computational costs by more than 50 %. In conclusion, we proposed a methodology to reduce the number of required simulations for data assimilation by aggregating data from fine to coarse resolutions. Our method was promising for applying data assimilation over large areas and benefiting more from satellite information for real-time prediction of agricultural productivity.}
}

L. Lärm, F. M. Bauer, J. van der Kruk, J. Vanderborght, S. Morandage, H. Vereecken, A. Schnepf, and A. Klotzsche, "Linking horizontal crosshole GPR variability with root image information for maize crops," Vadose Zone Journal, vol. 23, iss. 1, p. e20293, 2024. doi:10.1002/vzj2.20293
[BibTeX] [PDF]

@article{larm2024linking,
title={Linking horizontal crosshole GPR variability with root image information for maize crops},
author={L{\"a}rm, Lena and Bauer, Felix Maximilian and van der Kruk, Jan and Vanderborght, Jan and Morandage, Shehan and Vereecken, Harry and Schnepf, Andrea and Klotzsche, Anja},
journal={Vadose Zone Journal},
volume={23},
number={1},
pages={e20293},
year={2024},
publisher={Wiley Online Library},
doi={10.1002/vzj2.20293},
url={https://acsess.onlinelibrary.wiley.com/doi/full/10.1002/vzj2.20293}
}

S. Paulus, L. Pichler, and A. Barreto, "Using spectral sensing in plant science," Sugar Industry International, vol. 149, pp. 122-126, 2024. doi:10.36961/si30916
[BibTeX] [PDF]

@article{paulus2024sugarindustry,
author = {Paulus, Stefan AND Pichler, Lea AND Barreto, Abel},
title = {{Using spectral sensing in plant science}},
journal = {Sugar Industry International},
volume = {149},
issue = {2},
year = {2024},
doi = {10.36961/si30916},
pages = {122-126},
videourl = {},
url = {https://www.researchgate.net/publication/377774571_Using_spectral_sensing_in_plant_science/link/667962b41dec0c3c6f9fbc72/download?_tp=eyJjb250ZXh0Ijp7InBhZ2UiOiJwdWJsaWNhdGlvbiIsInByZXZpb3VzUGFnZSI6bnVsbH19},
keywords = {Agriculture, Disease detection, Plant monitoring, EdgeAI},
abstract = “{Digital cameras are widely used tools for plant monitoring in plant science today. Used to track plant growth or even visible symptoms, they are important tools for breeding and plant protection field trials. Nevertheless, its extension to measure the near infrared (NIR) region (700–1000 nm) includes great potential as plants show a higher light reflectance within this spectrum. Various applications have shown its use for disease detection, quantification, virus content estimation, and stress monitoring. As the next step is a comprehensive integration into agricultural routines, this study will show two use-cases with a high technological readiness level. One use-case shows a handheld multispectral sensor, which is used for manual measurements to detect and discriminate different virus types in sugar beet. In contrast, the second use-case shows a transfer to an UAV based disease quantification routine based on spectral imaging for Cercospora leaf spot. In addition, two prototypical workflows are shown for processing non-imaging and imaging spectral data in an agricultural setting. This study shows the state of the art in spectral sensing in the field for the two major sugar beet diseases– virus yellows and Cercospora leaf spot. Furthermore a future perspective for coming technological challenges regarding the integration of AI in sensors or robotic workflows is provided.}”,
}

Y. E. Mihiret, G. Schaaf, and M. Kamleitner, "Protein pyrophosphorylation by inositol phosphates: a novel post-translational modification in plants?," Frontiers in Plant Science, vol. 15, 2024. doi:10.3389/fpls.2024.1347922
[BibTeX] [PDF]

Inositol pyrophosphates (PP-InsPs) are energy-rich molecules harboring one or more diphosphate moieties. PP-InsPs are found in all eukaryotes evaluated and their functional versatility is reflected in the various cellular events in which they take part. These include, among others, insulin signaling and intracellular trafficking in mammals, as well as innate immunity and hormone and phosphate signaling in plants. The molecular mechanisms by which PP-InsPs exert such functions are proposed to rely on the allosteric regulation via direct binding to proteins, by competing with other ligands, or by protein pyrophosphorylation. The latter is the focus of this review, where we outline a historical perspective surrounding the first findings, almost 20 years ago, that certain proteins can be phosphorylated by PP-InsPs in vitro. Strikingly, in vitro phosphorylation occurs by an apparent enzyme-independent but Mg²⁺-dependent transfer of the β-phosphoryl group of an inositol pyrophosphate to an already phosphorylated serine residue at Glu/Asp-rich protein regions. Ribosome biogenesis, vesicle trafficking and transcription are among the cellular events suggested to be modulated by protein pyrophosphorylation in yeast and mammals. Here we discuss the latest efforts in identifying targets of protein pyrophosphorylation, pointing out the methodological challenges that have hindered the full understanding of this unique post-translational modification, and focusing on the latest advances in mass spectrometry that finally provided convincing evidence that PP-InsP-mediated pyrophosphorylation also occurs in vivo. We also speculate about the relevance of this post-translational modification in plants in a discussion centered around the protein kinase CK2, whose activity is critical for pyrophosphorylation of animal and yeast proteins. This enzyme is widely present in plant species and several of its functions overlap with those of PP-InsPs. Until now, there is virtually no data on pyrophosphorylation of plant proteins, which is an exciting field that remains to be explored.

@ARTICLE{10.3389/fpls.2024.1347922,
AUTHOR={Mihiret, Yeshambel Emewodih and Schaaf, Gabriel and Kamleitner, Marília },
TITLE={Protein pyrophosphorylation by inositol phosphates: a novel post-translational modification in plants?},
JOURNAL={Frontiers in Plant Science},
VOLUME={15},
YEAR={2024},
URL={https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2024.1347922},
DOI={10.3389/fpls.2024.1347922},
ISSN={1664-462X},
ABSTRACT={Inositol pyrophosphates (PP-InsPs) are energy-rich molecules harboring one or more diphosphate moieties. PP-InsPs are found in all eukaryotes evaluated and their functional versatility is reflected in the various cellular events in which they take part. These include, among others, insulin signaling and intracellular trafficking in mammals, as well as innate immunity and hormone and phosphate signaling in plants. The molecular mechanisms by which PP-InsPs exert such functions are proposed to rely on the allosteric regulation via direct binding to proteins, by competing with other ligands, or by protein pyrophosphorylation. The latter is the focus of this review, where we outline a historical perspective surrounding the first findings, almost 20 years ago, that certain proteins can be phosphorylated by PP-InsPs in vitro. Strikingly, in vitro phosphorylation occurs by an apparent enzyme-independent but Mg²⁺-dependent transfer of the β-phosphoryl group of an inositol pyrophosphate to an already phosphorylated serine residue at Glu/Asp-rich protein regions. Ribosome biogenesis, vesicle trafficking and transcription are among the cellular events suggested to be modulated by protein pyrophosphorylation in yeast and mammals. Here we discuss the latest efforts in identifying targets of protein pyrophosphorylation, pointing out the methodological challenges that have hindered the full understanding of this unique post-translational modification, and focusing on the latest advances in mass spectrometry that finally provided convincing evidence that PP-InsP-mediated pyrophosphorylation also occurs in vivo. We also speculate about the relevance of this post-translational modification in plants in a discussion centered around the protein kinase CK2, whose activity is critical for pyrophosphorylation of animal and yeast proteins. This enzyme is widely present in plant species and several of its functions overlap with those of PP-InsPs. Until now, there is virtually no data on pyrophosphorylation of plant proteins, which is an exciting field that remains to be explored.
}}

M. Rakotondramanana, M. Wissuwa, L. Ramanankaja, T. Razafimbelo, J. Stangoulis, and C. Grenier, "Stability of grain zinc concentrations across lowland rice environments favors zinc biofortification breeding," Frontiers in Plant Science, vol. 15, 2024. doi:10.3389/fpls.2024.1293831
[BibTeX] [PDF]

Introduction

One-third of the human population consumes insufficient zinc (Zn) to sustain a healthy life. Zn deficiency can be relieved by increasing the Zn concentration ([Zn]) in staple food crops through biofortification breeding. Rice is a poor source of Zn, and in countries predominantly relying on rice without sufficient dietary diversification, such as Madagascar, Zn biofortification is a priority.

Methods

Multi-environmental trials were performed in Madagascar over two years, 2019 and 2020, to screen a total of 28 genotypes including local and imported germplasm. The trials were conducted in the highlands of Ankazomiriotra, Anjiro, and Behenji and in Morovoay, a location representative of the coastal ecosystem. Contributions of genotype (G), environment (E), and G by E interactions (GEIs) were investigated.

Result

The grain [Zn] of local Malagasy rice varieties was similar to the internationally established grain [Zn] baseline of 18–20 μg/g for brown rice. While several imported breeding lines reached 50% of our breeding target set at +12 μg/g, only few met farmers’ appreciation criteria. Levels of grain [Zn] were stable across E. The G effects accounted for a main fraction of the variation, 76% to 83% of the variation for year 1 and year 2 trials, respectively, while GEI effects were comparatively small, contributing 23% to 9%. This contrasted with dominant E and GEI effects for grain yield. Our results indicate that local varieties tested contained insufficient Zn to alleviate Zn malnutrition, and developing new Zn-biofortified varieties should therefore be a priority. GGE analysis did not distinguish mega-environments for grain [Zn], whereas at least three mega-environments existed for grain yield, differentiated by the presence of limiting environmental conditions and responsiveness to improved soil fertility.

Discussion

Our main conclusion reveals that grain [Zn] seems to be under strong genetic control in the agro-climatic conditions of Madagascar. We could identify several interesting genotypes as potential donors for the breeding program, among those BF156, with a relatively stable grain [Zn] (AMMI stability value (ASV) = 0.89) reaching our target (>26 μg/g). While selection for grain yield, general adaptation, and farmers’ appreciation would have to rely on multi-environment testing, selection for grain [Zn] could be centralized in earlier generations.

@ARTICLE{10.3389/fpls.2024.1293831,
AUTHOR={Rakotondramanana, Mbolatantely and Wissuwa, Matthias and Ramanankaja, Landiarimisa and Razafimbelo, Tantely and Stangoulis, James and Grenier, Cécile },
TITLE={Stability of grain zinc concentrations across lowland rice environments favors zinc biofortification breeding},
JOURNAL={Frontiers in Plant Science},
VOLUME={15},
YEAR={2024},
URL={https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2024.1293831},
DOI={10.3389/fpls.2024.1293831},
ISSN={1664-462X},
ABSTRACT={IntroductionOne-third of the human population consumes insufficient zinc (Zn) to sustain a healthy life. Zn deficiency can be relieved by increasing the Zn concentration ([Zn]) in staple food crops through biofortification breeding. Rice is a poor source of Zn, and in countries predominantly relying on rice without sufficient dietary diversification, such as Madagascar, Zn biofortification is a priority.MethodsMulti-environmental trials were performed in Madagascar over two years, 2019 and 2020, to screen a total of 28 genotypes including local and imported germplasm. The trials were conducted in the highlands of Ankazomiriotra, Anjiro, and Behenji and in Morovoay, a location representative of the coastal ecosystem. Contributions of genotype (G), environment (E), and G by E interactions (GEIs) were investigated.ResultThe grain [Zn] of local Malagasy rice varieties was similar to the internationally established grain [Zn] baseline of 18–20 μg/g for brown rice. While several imported breeding lines reached 50% of our breeding target set at +12 μg/g, only few met farmers’ appreciation criteria. Levels of grain [Zn] were stable across E. The G effects accounted for a main fraction of the variation, 76% to 83% of the variation for year 1 and year 2 trials, respectively, while GEI effects were comparatively small, contributing 23% to 9%. This contrasted with dominant E and GEI effects for grain yield. Our results indicate that local varieties tested contained insufficient Zn to alleviate Zn malnutrition, and developing new Zn-biofortified varieties should therefore be a priority. GGE analysis did not distinguish mega-environments for grain [Zn], whereas at least three mega-environments existed for grain yield, differentiated by the presence of limiting environmental conditions and responsiveness to improved soil fertility.DiscussionOur main conclusion reveals that grain [Zn] seems to be under strong genetic control in the agro-climatic conditions of Madagascar. We could identify several interesting genotypes as potential donors for the breeding program, among those BF156, with a relatively stable grain [Zn] (AMMI stability value (ASV) = 0.89) reaching our target (>26 μg/g). While selection for grain yield, general adaptation, and farmers’ appreciation would have to rely on multi-environment testing, selection for grain [Zn] could be centralized in earlier generations.}}

A. Emam, M. Farag, and R. Roscher, "Confident Naturalness Explanation (CNE): A Framework to Explain and Assess Patterns Forming Naturalness," IEEE Geoscience and Remote Sensing Letters, pp. 1-1, 2024. doi:10.1109/LGRS.2024.3365196
[BibTeX] [PDF] [Code]

@ARTICLE{10433174,
author={Emam, Ahmed and Farag, Mohamed and Roscher, Ribana},
journal={IEEE Geoscience and Remote Sensing Letters},
title={Confident Naturalness Explanation (CNE): A Framework to Explain and Assess Patterns Forming Naturalness},
year={2024},
volume={},
number={},
pages={1-1},
codeurl={https://github.com/ahmedemam576/confident_explanations/blob/main/README.md},
url={https://arxiv.org/pdf/2311.08936},
keywords={Uncertainty;Measurement;Predictive models;Data models;Satellite images;Logistic regression;Land surface;Explainable machine learning;uncertainty quantification;remote sensing;pattern recognition;naturalness index},
doi={10.1109/LGRS.2024.3365196}}

P. Feisthauer, M. Hartmann, and J. Börner, "Behavioral factors driving farmers’ intentions to adopt spot spraying for sustainable weed control," Journal of Environmental Management, vol. 353, p. 120218, 2024. doi:10.1016/j.jenvman.2024.120218
[BibTeX] [PDF]

Smart Farming Technologies enable plant-specific agrochemical applications which can increase the efficiency and reduce the environmental impacts of agriculture. However, the uptake of Smart Farming Technologies remains slow despite their potential to enhance sustainable transformation of food systems. The design of policies to promote sustainable agricultural technologies requires a holistic understanding of the complex set of factors driving the adoption of innovations at farm level. This study has a focus on behavioral factors, such as pro-environmental attitude, personal innovativeness and moral norms. Based on an online study conducted in Germany, structural equation modelling is applied to test the predictions of an extended version of the Theory of Planned Behavior, using spot spraying, a smart weeding technology, as an example. The results confirm theoretical predictions and show that indicators of attitude, subjective norms, and perceived behavioral control have relevant effects on farmers’ adoption intentions. The extended model revealed a medium-sized (small) direct effect of moral norms on the attitude towards spot spraying (adoption intention). Personal innovativeness had a small effect on adoption intention, whereas pro-environmental attitude did not exhibit a clear direction of impact. Methodological and policy implications derived from the results are discussed noting that the inclusion of indicators for moral norms can improve the predictive power of models used in future research in this field. Overall, initiatives aimed at facilitating the exchange of opinions and related moral norms as well as collaboration among peers may contribute to voluntary sustainable innovation as it enhances adoption intentions among farmers.

@article{FEISTHAUER2024120218,
title = {Behavioral factors driving farmers’ intentions to adopt spot spraying for sustainable weed control},
journal = {Journal of Environmental Management},
volume = {353},
pages = {120218},
year = {2024},
issn = {0301-4797},
doi = {10.1016/j.jenvman.2024.120218},
url = {https://www.sciencedirect.com/science/article/pii/S0301479724002044},
author = {Philipp Feisthauer and Monika Hartmann and Jan Börner},
keywords = {Smart farming technologies, Sustainable intensification, Partial least squares structural equation modelling, Voluntary technology uptake, Agricultural policy},
abstract = {Smart Farming Technologies enable plant-specific agrochemical applications which can increase the efficiency and reduce the environmental impacts of agriculture. However, the uptake of Smart Farming Technologies remains slow despite their potential to enhance sustainable transformation of food systems. The design of policies to promote sustainable agricultural technologies requires a holistic understanding of the complex set of factors driving the adoption of innovations at farm level. This study has a focus on behavioral factors, such as pro-environmental attitude, personal innovativeness and moral norms. Based on an online study conducted in Germany, structural equation modelling is applied to test the predictions of an extended version of the Theory of Planned Behavior, using spot spraying, a smart weeding technology, as an example. The results confirm theoretical predictions and show that indicators of attitude, subjective norms, and perceived behavioral control have relevant effects on farmers’ adoption intentions. The extended model revealed a medium-sized (small) direct effect of moral norms on the attitude towards spot spraying (adoption intention). Personal innovativeness had a small effect on adoption intention, whereas pro-environmental attitude did not exhibit a clear direction of impact. Methodological and policy implications derived from the results are discussed noting that the inclusion of indicators for moral norms can improve the predictive power of models used in future research in this field. Overall, initiatives aimed at facilitating the exchange of opinions and related moral norms as well as collaboration among peers may contribute to voluntary sustainable innovation as it enhances adoption intentions among farmers.}
}

P. Feisthauer, M. Hartmann, and J. Börner, "Adoption intentions of smart weeding technologies—A lab-in-the-field experiment with German crop farmers," Q Open, vol. 4, iss. 1, p. qoae002, 2024. doi:10.1093/qopen/qoae002
[BibTeX] [PDF]

{Smart weeding technologies (SWT) enable substantial herbicide savings via precise sensor-based application. This can enhance agrobiodiversity and make modern agriculture more sustainable. Currently, our knowledge about what will determine SWT adoption at the farm level is limited because few mature and economically viable prototype systems are available. We conduct a pre-registered and incentive-compatible online lab-in-the-field experiment with a convenience sample of 334 active German crop farmers to assess whether pro-environmental attitude, innovativeness, and trust in farming data privacy explain hypothetical SWT adoption. We further test if an environmentally motivated subsidy, a green nudge, and a combination thereof affect adoption intentions. While attitudinal measures clearly modulate hypothetical adoption decisions in our sample, we detect no effect for the nudge and subsidy. Our findings have implications for policy and future research. Substantial policy support may be needed as long as environmentally beneficial smart farming technology remains privately less competitive than conventional alternatives. Moreover, targeting criteria for early adopters include pro-environmental attitudes and innovativeness.}

@article{10.1093/qopen/qoae002,
author = {Feisthauer, Philipp and Hartmann, Monika and Börner, Jan},
title = "{Adoption intentions of smart weeding technologies—A lab-in-the-field experiment with German crop farmers}",
journal = {Q Open},
volume = {4},
number = {1},
pages = {qoae002},
year = {2024},
month = {01},
abstract = "{Smart weeding technologies (SWT) enable substantial herbicide savings via precise sensor-based application. This can enhance agrobiodiversity and make modern agriculture more sustainable. Currently, our knowledge about what will determine SWT adoption at the farm level is limited because few mature and economically viable prototype systems are available. We conduct a pre-registered and incentive-compatible online lab-in-the-field experiment with a convenience sample of 334 active German crop farmers to assess whether pro-environmental attitude, innovativeness, and trust in farming data privacy explain hypothetical SWT adoption. We further test if an environmentally motivated subsidy, a green nudge, and a combination thereof affect adoption intentions. While attitudinal measures clearly modulate hypothetical adoption decisions in our sample, we detect no effect for the nudge and subsidy. Our findings have implications for policy and future research. Substantial policy support may be needed as long as environmentally beneficial smart farming technology remains privately less competitive than conventional alternatives. Moreover, targeting criteria for early adopters include pro-environmental attitudes and innovativeness.}",
issn = {2633-9048},
doi = {10.1093/qopen/qoae002},
url = {https://doi.org/10.1093/qopen/qoae002},
eprint = {https://academic.oup.com/qopen/article-pdf/4/1/qoae002/56614489/qoae002.pdf},
}

A. Emam, T. T. Stomberg, and R. Roscher, "Leveraging Activation Maximization and Generative Adversarial Training to Recognize and Explain Patterns in Natural Areas in Satellite Imagery," IEEE Geoscience and Remote Sensing Letters, vol. 21, pp. 1-5, 2024. doi:10.1109/LGRS.2023.3335473
[BibTeX] [PDF] [Code]

@ARTICLE{10325539,
author={Emam, Ahmed and Stomberg, Timo T. and Roscher, Ribana},
journal={IEEE Geoscience and Remote Sensing Letters},
title={Leveraging Activation Maximization and Generative Adversarial Training to Recognize and Explain Patterns in Natural Areas in Satellite Imagery},
year={2024},
volume={21},
number={},
pages={1-5},
doi={10.1109/LGRS.2023.3335473},
codeurl={https://github.com/ahmedemam576/SpacEX},
url={https://arxiv.org/pdf/2311.08923}}

M. L. Zingsheim and T. F. Döring, "What weeding robots need to know about ecology," Agriculture, Ecosystems & Environment, vol. 364, p. 108861, 2024. doi:10.1016/j.agee.2023.108861
[BibTeX] [PDF]

In weed control the aims of securing crop productivity and protecting biodiversity are often difficult to reconcile. Currently, the development of autonomous in-field intervention technology, such as field robots, is creating new potential for minimizing trade-offs between these two aims. To exploit this potential, weed management strategies need to adapt. However, it is currently unclear which kind of input information (e.g. weed cover, number of weeds, weed species identity) is required for such a targeted approach, and which impacts the robotic application has on the trade-off between crop yield and biodiversity. Here, we used a dataset from organically farmed fields to assess several weed management strategies, simulating robot-supported weed control. Specifically, we used within-field heterogeneity of several weed and crop productivity variables to model effects of different kinds of input information for a hypothetical, spatially selective robotic weed control system. The results showed that, at a defined yield loss, gamma diversity (number of weed species on the entire investigated area) is maintainable to a large degree, even without information on weed or crop heterogeneity within the field being used to decide where to weed. However, to maintain alpha diversity (average number of weed species per plot), more spatially explicit input information is required, such as on the number of species per plot, weed quantity (weed cover per species), and weed competitiveness. Consequently, a weeding robot would have to be technically capable of distinguishing between individual weed species, measuring weed cover, processing captured information in real time and removing weeds at per-plant level. Further, it could be shown that the success of such a complex weed management strategy is independent of the degree of spatial heterogeneity of crop yield and of the present level of weed species richness.

@article{ZINGSHEIM2024108861,
title = {What weeding robots need to know about ecology},
journal = {Agriculture, Ecosystems & Environment},
volume = {364},
pages = {108861},
year = {2024},
issn = {0167-8809},
doi = {10.1016/j.agee.2023.108861},
url = {https://www.sciencedirect.com/science/article/pii/S0167880923005200},
author = {Marie L. Zingsheim and Thomas F. Döring},
keywords = {Autonomous in-field intervention, Biodiversity conservation, Traits, Integrated weed control, Weed technology},
abstract = {In weed control the aims of securing crop productivity and protecting biodiversity are often difficult to reconcile. Currently, the development of autonomous in-field intervention technology, such as field robots, is creating new potential for minimizing trade-offs between these two aims. To exploit this potential, weed management strategies need to adapt. However, it is currently unclear which kind of input information (e.g. weed cover, number of weeds, weed species identity) is required for such a targeted approach, and which impacts the robotic application has on the trade-off between crop yield and biodiversity. Here, we used a dataset from organically farmed fields to assess several weed management strategies, simulating robot-supported weed control. Specifically, we used within-field heterogeneity of several weed and crop productivity variables to model effects of different kinds of input information for a hypothetical, spatially selective robotic weed control system. The results showed that, at a defined yield loss, gamma diversity (number of weed species on the entire investigated area) is maintainable to a large degree, even without information on weed or crop heterogeneity within the field being used to decide where to weed. However, to maintain alpha diversity (average number of weed species per plot), more spatially explicit input information is required, such as on the number of species per plot, weed quantity (weed cover per species), and weed competitiveness. Consequently, a weeding robot would have to be technically capable of distinguishing between individual weed species, measuring weed cover, processing captured information in real time and removing weeds at per-plant level. Further, it could be shown that the success of such a complex weed management strategy is independent of the degree of spatial heterogeneity of crop yield and of the present level of weed species richness.}
}

H. Storm, S. J. Seidel, L. Klingbeil, F. Ewert, H. Vereecken, W. Amelung, S. Behnke, M. Bennewitz, J. Börner, T. Döring, J. Gall, A. -K. Mahlein, C. McCool, U. Rascher, S. Wrobel, A. Schnepf, C. Stachniss, and H. Kuhlmann, "Research Priorities to Leverage Smart Digital Technologies for Sustainable Crop Production," European Journal of Agronomy, vol. 156, p. 127178, 2024. doi:10.1016/j.eja.2024.127178
[BibTeX] [PDF]

@article{storm2024eja,
author = {H. Storm and S.J. Seidel and L. Klingbeil and F. Ewert and H. Vereecken and W. Amelung and S. Behnke and M. Bennewitz and J. B\"orner and T. D\"oring and J. Gall and A.-K. Mahlein and C. McCool and U. Rascher and S. Wrobel and A. Schnepf and C. Stachniss and H. Kuhlmann},
title = {{Research Priorities to Leverage Smart Digital Technologies for Sustainable Crop Production}},
journal = {European Journal of Agronomy},
volume = {156},
pages = {127178},
year = {2024},
issn = {1161-0301},
doi = {10.1016/j.eja.2024.127178},
url = {https://www.sciencedirect.com/science/article/pii/S1161030124000996},
}

I. Hroob, B. Mersch, C. Stachniss, and M. Hanheide, "Generalizable Stable Points Segmentation for 3D LiDAR Scan-to-Map Long-Term Localization," IEEE Robotics and Automation Letters (RA-L), vol. 9, iss. 4, pp. 3546-3553, 2024. doi:10.1109/LRA.2024.3368236
[BibTeX] [PDF] [Code] [Video]

@article{hroob2024ral,
author = {I. Hroob and B. Mersch and C. Stachniss and M. Hanheide},
title = {{Generalizable Stable Points Segmentation for 3D LiDAR Scan-to-Map Long-Term Localization}},
journal = {IEEE Robotics and Automation Letters (RA-L)},
volume = {9},
number = {4},
pages = {3546-3553},
year = 2024,
doi = {10.1109/LRA.2024.3368236},
codeurl = {https://github.com/ibrahimhroob/SPS},
videourl = {https://www.youtube.com/watch?v=aRLStFQEXbc},
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/hroob2024ral.pdf}
}

M. V. R. Malladi, T. Guadagnino, L. Lobefaro, M. Mattamala, H. Griess, J. Schweier, N. Chebrolu, M. Fallon, J. Behley, and C. Stachniss, "Tree Instance Segmentation and Traits Estimation for Forestry Environments Exploiting LiDAR Data Collected by Mobile Robots," in Proc.~of the IEEE Intl.~Conf.~on Robotics & Automation (ICRA) , 2024.
[BibTeX] [PDF] [Code] [Video]

@inproceedings{malladi2024icra,
author = {M.V.R. Malladi and T. Guadagnino and L. Lobefaro and M. Mattamala and H. Griess and J. Schweier and N. Chebrolu and M. Fallon and J. Behley and C. Stachniss},
title = {{Tree Instance Segmentation and Traits Estimation for Forestry Environments Exploiting LiDAR Data Collected by Mobile Robots}},
booktitle = {Proc.~of the IEEE Intl.~Conf.~on Robotics \& Automation (ICRA)},
year = 2024,
codeurl = {https://github.com/PRBonn/forest_inventory_pipeline},
videourl = {https://www.youtube.com/watch?v=DQDSBg94B-g},
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/malladi2024icra.pdf}
}

J. Rückin, F. Magistri, C. Stachniss, and M. Popović, "Semi-Supervised Active Learning for Semantic Segmentation in Unknown Environments Using Informative Path Planning," IEEE Robotics and Automation Letters (RA-L), vol. 9, iss. 3, pp. 2662-2669, 2024. doi:10.1109/LRA.2024.3359970
[BibTeX] [PDF] [Code]

@article{rueckin2024ral,
author = {J. R\"uckin and F. Magistri and C. Stachniss and M. Popovi\'c},
title = {{Semi-Supervised Active Learning for Semantic Segmentation in Unknown Environments Using Informative Path Planning}},
journal = {IEEE Robotics and Automation Letters (RA-L)},
year = {2024},
volume = {9},
number = {3},
pages = {2662-2669},
issn = {2377-3766},
doi = {10.1109/LRA.2024.3359970},
codeurl = {https://github.com/dmar-bonn/ipp-ssl},
url = {https://arxiv.org/pdf/2312.04402}
}

M. R. Paul, D. T. Demie, S. J. Seidel, and T. F. Döring, "Evaluation of multiple spring wheat cultivars in diverse intercropping systems," European Journal of Agronomy, vol. 152, p. 127024, 2024. doi:10.1016/j.eja.2023.127024
[BibTeX] [PDF]

Re-diversifying agriculture through cereal-legume intercropping could provide higher productivity, but the cultivars and traits associated with agronomic performance of intercropping are currently unclear. Moreover, the impact of different cultivars on intercropping performance under diverse environmental and management conditions is largely unexplored. Therefore, we investigated various cultivar combinations of spring wheat (SW, Triticum aestivum L.) and faba bean (FB, Vicia faba L.) under diverse intercropping conditions, and evaluated their mixing abilities. We used variability in terms of plant height among cultivars and assessed the effects of height on total grain yield in the mixture. Twelve entries (ten cultivars and two cultivar mixtures) of SW and two cultivars of FB were sown as sole crops and all possible 1:1 species mixtures, with each of these treatments grown at two sowing densities and in three environments. Our results did not show any significant main effect of SW and FB cultivars on the total grain yield in the mixture. However, there were significant two-way interaction effects of SW cultivars with all the other factors (environment, FB cultivar, and sowing density) on the mixtures’ grain yield advantage over sole crops. Spearman’s rank correlation (ρ) among SW cultivars between SW yield in monoculture and total yield in mixture was variable, depending on the trial environment with ρ ranging from 0.42 (non-significant) to 0.90 (P < 0.001). In addition, a highly significant effect of environments and crop densities on the total grain yield in the mixture was observed. A significant but weak positive correlation of SW plant height with the total grain yield in the mixture was detected in one of the three environments. We conclude that selecting a site-specific partner combination of cereals and legumes with appropriate management practices is a key element to enhance functional complementarity and improve total productivity in intercropping, though the complexity of the interactions makes this a difficult task.

@article{PAUL2024127024,
title = {Evaluation of multiple spring wheat cultivars in diverse intercropping systems},
journal = {European Journal of Agronomy},
volume = {152},
pages = {127024},
year = {2024},
issn = {1161-0301},
doi = {10.1016/j.eja.2023.127024},
url = {https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4564794},
author = {Madhuri R. Paul and Dereje T. Demie and Sabine J. Seidel and Thomas F. Döring},
keywords = {Crop mixture, Crop synergy, Diversification, Sustainable agriculture, Competition, Cooperation, Compensation, Varietal choice},
abstract = {Re-diversifying agriculture through cereal-legume intercropping could provide higher productivity, but the cultivars and traits associated with agronomic performance of intercropping are currently unclear. Moreover, the impact of different cultivars on intercropping performance under diverse environmental and management conditions is largely unexplored. Therefore, we investigated various cultivar combinations of spring wheat (SW, Triticum aestivum L.) and faba bean (FB, Vicia faba L.) under diverse intercropping conditions, and evaluated their mixing abilities. We used variability in terms of plant height among cultivars and assessed the effects of height on total grain yield in the mixture. Twelve entries (ten cultivars and two cultivar mixtures) of SW and two cultivars of FB were sown as sole crops and all possible 1:1 species mixtures, with each of these treatments grown at two sowing densities and in three environments. Our results did not show any significant main effect of SW and FB cultivars on the total grain yield in the mixture. However, there were significant two-way interaction effects of SW cultivars with all the other factors (environment, FB cultivar, and sowing density) on the mixtures’ grain yield advantage over sole crops. Spearman’s rank correlation (ρ) among SW cultivars between SW yield in monoculture and total yield in mixture was variable, depending on the trial environment with ρ ranging from 0.42 (non-significant) to 0.90 (P < 0.001). In addition, a highly significant effect of environments and crop densities on the total grain yield in the mixture was observed. A significant but weak positive correlation of SW plant height with the total grain yield in the mixture was detected in one of the three environments. We conclude that selecting a site-specific partner combination of cereals and legumes with appropriate management practices is a key element to enhance functional complementarity and improve total productivity in intercropping, though the complexity of the interactions makes this a difficult task.}
}

S. L. Bauke, S. J. Seidel, M. Athmann, A. E. Berns, M. Braun, M. I. Gocke, J. Guigue, T. Kautz, I. Kögel-Knabner, J. Ohan, M. Rillig, M. Schloter, O. Schmittmann, S. Schulz, D. Uhlig, A. Schnepf, and W. Amelung, "Short-term effects of subsoil management by strip-wise loosening and incorporation of organic material," Soil and Tillage Research, vol. 236, p. 105936, 2024. doi:10.1016/j.still.2023.105936
[BibTeX] [PDF]

Agricultural production in Central Europe increasingly suffers from extreme drought events. Improving root access to nutrient and water resources in the subsoil below the plow layer is a potential option to maintain productivity during dry summers. Here, we tested a strip-wise subsoil amelioration method that combines subsoil loosening with organic matter incorporation into the subsoil (biowaste or green waste compost) and compared it with a treatment of only subsoil loosening and a non-ameliorated control. A field experiment with randomized block design was conducted on a Luvisol with an argic horizon (Bt), with a rotation of spring barley and winter wheat. In the first two years after amelioration, we monitored soil physico-chemical parameters, microbial biomass, and shoot and root growth at anthesis as well as harvested grain yield and quality. Subsoil loosening with organic matter incorporation significantly decreased soil bulk density at the depth of compost incorporation when biowaste compost was used, but not when green waste compost had been incorporated. Nutrient stocks, nutrient availability and microbial biomass were not consistently affected by the subsoil amelioration. Nevertheless, the incorporation of organic material, especially biowaste compost, significantly increased root growth into the subsoil and subsequently significantly enhanced crop nutrient uptake, biomass and grain yield production. Green waste compost incorporation had less pronounced effects, with an increase in grain yield only in the second year after amelioration. Differences in crop development could not be explained by any single soil parameter, suggesting that it was rather a combined effect of loosened subsoil and better supply of subsoil resources that resulted in an increase in subsoil root length density and subsequently led to better crop performance.

@article{BAUKE2024105936,
title = {Short-term effects of subsoil management by strip-wise loosening and incorporation of organic material},
journal = {Soil and Tillage Research},
volume = {236},
pages = {105936},
year = {2024},
issn = {0167-1987},
doi = {10.1016/j.still.2023.105936},
url = {https://www.phenorob.de/wp-content/uploads/2024/08/Crop_performance_rev2_clean.pdf},
author = {Sara L. Bauke and Sabine J. Seidel and Miriam Athmann and Anne E. Berns and Melanie Braun and Martina I. Gocke and Julien Guigue and Timo Kautz and Ingrid Kögel-Knabner and Juliette Ohan and Matthias Rillig and Michael Schloter and Oliver Schmittmann and Stefanie Schulz and David Uhlig and Andrea Schnepf and Wulf Amelung},
keywords = {Subsoil management, Microbial biomass, Soil nutrients, Stable isotopes, Root growth, Grain yield},
abstract = {Agricultural production in Central Europe increasingly suffers from extreme drought events. Improving root access to nutrient and water resources in the subsoil below the plow layer is a potential option to maintain productivity during dry summers. Here, we tested a strip-wise subsoil amelioration method that combines subsoil loosening with organic matter incorporation into the subsoil (biowaste or green waste compost) and compared it with a treatment of only subsoil loosening and a non-ameliorated control. A field experiment with randomized block design was conducted on a Luvisol with an argic horizon (Bt), with a rotation of spring barley and winter wheat. In the first two years after amelioration, we monitored soil physico-chemical parameters, microbial biomass, and shoot and root growth at anthesis as well as harvested grain yield and quality. Subsoil loosening with organic matter incorporation significantly decreased soil bulk density at the depth of compost incorporation when biowaste compost was used, but not when green waste compost had been incorporated. Nutrient stocks, nutrient availability and microbial biomass were not consistently affected by the subsoil amelioration. Nevertheless, the incorporation of organic material, especially biowaste compost, significantly increased root growth into the subsoil and subsequently significantly enhanced crop nutrient uptake, biomass and grain yield production. Green waste compost incorporation had less pronounced effects, with an increase in grain yield only in the second year after amelioration. Differences in crop development could not be explained by any single soil parameter, suggesting that it was rather a combined effect of loosened subsoil and better supply of subsoil resources that resulted in an increase in subsoil root length density and subsequently led to better crop performance.}
}

2023

R. Rahim, O. Esmaeelipoor Jahromi, W. Amelung, and E. Kroener, "Rhizosheath formation depends on mucilage concentration and water content," Plant and Soil, vol. 495, pp. 1-13, 2023. doi:10.1007/s11104-023-06353-4
[BibTeX] [PDF]

@article{article,
author = {Rahim, Riffat and Esmaeelipoor Jahromi, Omid and Amelung, Wulf and Kroener, Eva},
year = {2023},
month = {11},
pages = {1-13},
title = {Rhizosheath formation depends on mucilage concentration and water content},
volume = {495},
journal = {Plant and Soil},
doi = {10.1007/s11104-023-06353-4},
url = {https://link.springer.com/article/10.1007/s11104-023-06353-4},
}

D. N. Baker, F. M. Bauer, M. Giraud, A. Schnepf, J. H. Göbbert, H. Scharr, E. Þ. Hvannberg, and M. Riedel, "A scalable pipeline to create synthetic datasets from functional–structural plant models for deep learning," in silico Plants, vol. 6, iss. 1, p. diad022, 2023. doi:10.1093/insilicoplants/diad022
[BibTeX]

{In plant science, it is an established method to obtain structural parameters of crops using image analysis. In recent years, deep learning techniques have improved the underlying processes significantly. However, since data acquisition is time and resource consuming, reliable training data are currently limited. To overcome this bottleneck, synthetic data are a promising option for not only enabling a higher order of correctness by offering more training data but also for validation of results. However, the creation of synthetic data is complex and requires extensive knowledge in Computer Graphics, Visualization and High-Performance Computing. We address this by introducing Synavis, a framework that allows users to train networks on real-time generated data. We created a pipeline that integrates realistic plant structures, simulated by the functional–structural plant model framework CPlantBox, into the game engine Unreal Engine. For this purpose, we needed to extend CPlantBox by introducing a new leaf geometrization that results in realistic leafs. All parameterized geometries of the plant are directly provided by the plant model. In the Unreal Engine, it is possible to alter the environment. WebRTC enables the streaming of the final image composition, which, in turn, can then be directly used to train deep neural networks to increase parameter robustness, for further plant trait detection and validation of original parameters. We enable user-friendly ready-to-use pipelines, providing virtual plant experiment and field visualizations, a python-binding library to access synthetic data and a ready-to-run example to train models.}

@article{10.1093/insilicoplants/diad022,
author = {Baker, Dirk Norbert and Bauer, Felix Maximilian and Giraud, Mona and Schnepf, Andrea and Göbbert, Jens Henrik and Scharr, Hanno and Hvannberg, Ebba Þora and Riedel, Morris},
title = "{A scalable pipeline to create synthetic datasets from functional–structural plant models for deep learning}",
journal = {in silico Plants},
volume = {6},
number = {1},
pages = {diad022},
year = {2023},
month = {12},
abstract = "{In plant science, it is an established method to obtain structural parameters of crops using image analysis. In recent years, deep learning techniques have improved the underlying processes significantly. However, since data acquisition is time and resource consuming, reliable training data are currently limited. To overcome this bottleneck, synthetic data are a promising option for not only enabling a higher order of correctness by offering more training data but also for validation of results. However, the creation of synthetic data is complex and requires extensive knowledge in Computer Graphics, Visualization and High-Performance Computing. We address this by introducing Synavis, a framework that allows users to train networks on real-time generated data. We created a pipeline that integrates realistic plant structures, simulated by the functional–structural plant model framework CPlantBox, into the game engine Unreal Engine. For this purpose, we needed to extend CPlantBox by introducing a new leaf geometrization that results in realistic leafs. All parameterized geometries of the plant are directly provided by the plant model. In the Unreal Engine, it is possible to alter the environment. WebRTC enables the streaming of the final image composition, which, in turn, can then be directly used to train deep neural networks to increase parameter robustness, for further plant trait detection and validation of original parameters. We enable user-friendly ready-to-use pipelines, providing virtual plant experiment and field visualizations, a python-binding library to access synthetic data and a ready-to-run example to train models.}",
issn = {2517-5025},
doi = {10.1093/insilicoplants/diad022},
}

J. Bendig, B. Siegmann, C. Kneer, E. Chakhvashvili, J. Kraemer, S. Choza-Farias, and U. Rascher, "Imaging Spatial Heterogeneity of Solar-Induced Chlorophyll Fluorescence (SIF) with Very High Spatial Resolution Drone Imagery," in IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium , 2023, pp. 4654-4657. doi:10.1109/IGARSS52108.2023.10283066
[BibTeX] [PDF]

@INPROCEEDINGS{10283066,
author={Bendig, J. and Siegmann, B. and Kneer, C. and Chakhvashvili, E. and Kraemer, J. and Choza-Farias, S. and Rascher, U.},
booktitle={IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium},
title={Imaging Spatial Heterogeneity of Solar-Induced Chlorophyll Fluorescence (SIF) with Very High Spatial Resolution Drone Imagery},
year={2023},
volume={},
number={},
pages={4654-4657},
keywords={Reflectivity;Optical filters;System performance;Vegetation mapping;Prototypes;Fluorescence;Cameras;multi camera array;uncrewed aerial system;HyPlant;FloX},
url={https://juser.fz-juelich.de/record/1017395/files/Bendig_IG23_paper_4586_final.pdf?version=1},
doi={10.1109/IGARSS52108.2023.10283066}}

J. Romer, K. Gutbrod, A. Schuppener, M. Melzer, S. J. Müller-Schüssele, A. J. Meyer, and P. Dörmann, "Tocopherol and phylloquinone biosynthesis in chloroplasts requires the phytol kinase VITAMIN E PATHWAY GENE5 (VTE5) and the farnesol kinase (FOLK)," The Plant Cell, vol. 36, iss. 4, pp. 1140-1158, 2023. doi:10.1093/plcell/koad316
[BibTeX]

{Chlorophyll degradation causes the release of phytol, which is converted into phytyl diphosphate (phytyl-PP) by phytol kinase (VITAMIN E PATHWAY GENE5 [VTE5]) and phytyl phosphate (phytyl-P) kinase (VTE6). The kinase pathway is important for tocopherol synthesis, as the Arabidopsis (Arabidopsis thaliana) vte5 mutant contains reduced levels of tocopherol. Arabidopsis harbors one paralog of VTE5, farnesol kinase (FOLK) involved in farnesol phosphorylation. Here, we demonstrate that VTE5 and FOLK harbor kinase activities for phytol, geranylgeraniol, and farnesol with different specificities. While the tocopherol content of the folk mutant is unchanged, vte5-2 folk plants completely lack tocopherol. Tocopherol deficiency in vte5-2 plants can be complemented by overexpression of FOLK, indicating that FOLK is an authentic gene of tocopherol synthesis. The vte5-2 folk plants contain only ∼40\\% of wild-type amounts of phylloquinone, demonstrating that VTE5 and FOLK both contribute in part to phylloquinone synthesis. Tocotrienol and menaquinone-4 were produced in vte5-2 folk plants after supplementation with homogentisate or 1,4-dihydroxy-2-naphthoic acid, respectively, indicating that their synthesis is independent of the VTE5/FOLK pathway. These results show that phytyl moieties for tocopherol synthesis are completely but, for phylloquinone production, only partially derived from geranylgeranyl-chlorophyll and phytol phosphorylation by VTE5 and FOLK.}

@article{10.1093/plcell/koad316,
author = {Romer, Jill and Gutbrod, Katharina and Schuppener, Antonia and Melzer, Michael and Müller-Schüssele, Stefanie J and Meyer, Andreas J and Dörmann, Peter},
title = "{Tocopherol and phylloquinone biosynthesis in chloroplasts requires the phytol kinase VITAMIN E PATHWAY GENE5 (VTE5) and the farnesol kinase (FOLK)}",
journal = {The Plant Cell},
volume = {36},
number = {4},
pages = {1140-1158},
year = {2023},
month = {12},
abstract = "{Chlorophyll degradation causes the release of phytol, which is converted into phytyl diphosphate (phytyl-PP) by phytol kinase (VITAMIN E PATHWAY GENE5 [VTE5]) and phytyl phosphate (phytyl-P) kinase (VTE6). The kinase pathway is important for tocopherol synthesis, as the Arabidopsis (Arabidopsis thaliana) vte5 mutant contains reduced levels of tocopherol. Arabidopsis harbors one paralog of VTE5, farnesol kinase (FOLK) involved in farnesol phosphorylation. Here, we demonstrate that VTE5 and FOLK harbor kinase activities for phytol, geranylgeraniol, and farnesol with different specificities. While the tocopherol content of the folk mutant is unchanged, vte5-2 folk plants completely lack tocopherol. Tocopherol deficiency in vte5-2 plants can be complemented by overexpression of FOLK, indicating that FOLK is an authentic gene of tocopherol synthesis. The vte5-2 folk plants contain only ∼40\\% of wild-type amounts of phylloquinone, demonstrating that VTE5 and FOLK both contribute in part to phylloquinone synthesis. Tocotrienol and menaquinone-4 were produced in vte5-2 folk plants after supplementation with homogentisate or 1,4-dihydroxy-2-naphthoic acid, respectively, indicating that their synthesis is independent of the VTE5/FOLK pathway. These results show that phytyl moieties for tocopherol synthesis are completely but, for phylloquinone production, only partially derived from geranylgeranyl-chlorophyll and phytol phosphorylation by VTE5 and FOLK.}",
issn = {1040-4651},
doi = {10.1093/plcell/koad316},
}

E. I. Katche, A. Schierholt, S. Schiessl, F. He, Z. Lv, J. Batley, H. C. Becker, and A. S. Mason, "Genetic factors inherited from both diploid parents interact to affect genome stability and fertility in resynthesized allotetraploid Brassica napus," G3 Genes|Genomes|Genetics, vol. 13, iss. 8, p. jkad136, 2023. doi:10.1093/g3journal/jkad136
[BibTeX] [PDF]

{Established allopolyploids are known to be genomically stable and fertile. However, in contrast, most newly resynthesized allopolyploids are infertile and meiotically unstable. Identifying the genetic factors responsible for genome stability in newly formed allopolyploid is key to understanding how 2 genomes come together to form a species. One hypothesis is that established allopolyploids may have inherited specific alleles from their diploid progenitors which conferred meiotic stability. Resynthesized Brassica napus lines are often unstable and infertile, unlike B. napus cultivars. We tested this hypothesis by characterizing 41 resynthesized B. napus lines produced by crosses between 8 Brassica rapa and 8 Brassica oleracea lines for copy number variation resulting from nonhomologous recombination events and fertility. We resequenced 8 B. rapa and 5 B. oleracea parent accessions and analyzed 19 resynthesized lines for allelic variation in a list of meiosis gene homologs. SNP genotyping was performed using the Illumina Infinium Brassica 60K array for 3 individuals per line. Self-pollinated seed set and genome stability (number of copy number variants) were significantly affected by the interaction between both B. rapa and B. oleracea parental genotypes. We identified 13 putative meiosis gene candidates which were significantly associated with frequency of copy number variants and which contained putatively harmful mutations in meiosis gene haplotypes for further investigation. Our results support the hypothesis that allelic variants inherited from parental genotypes affect genome stability and fertility in resynthesized rapeseed.}

@article{10.1093/g3journal/jkad136,
author = {Katche, Elizabeth Ihien and Schierholt, Antje and Schiessl, Sarah-Veronica and He, Fei and Lv, Zhenling and Batley, Jacqueline and Becker, Heiko C and Mason, Annaliese S},
title = "{Genetic factors inherited from both diploid parents interact to affect genome stability and fertility in resynthesized allotetraploid Brassica napus}",
journal = {G3 Genes|Genomes|Genetics},
volume = {13},
number = {8},
pages = {jkad136},
year = {2023},
month = {06},
abstract = "{Established allopolyploids are known to be genomically stable and fertile. However, in contrast, most newly resynthesized allopolyploids are infertile and meiotically unstable. Identifying the genetic factors responsible for genome stability in newly formed allopolyploid is key to understanding how 2 genomes come together to form a species. One hypothesis is that established allopolyploids may have inherited specific alleles from their diploid progenitors which conferred meiotic stability. Resynthesized Brassica napus lines are often unstable and infertile, unlike B. napus cultivars. We tested this hypothesis by characterizing 41 resynthesized B. napus lines produced by crosses between 8 Brassica rapa and 8 Brassica oleracea lines for copy number variation resulting from nonhomologous recombination events and fertility. We resequenced 8 B. rapa and 5 B. oleracea parent accessions and analyzed 19 resynthesized lines for allelic variation in a list of meiosis gene homologs. SNP genotyping was performed using the Illumina Infinium Brassica 60K array for 3 individuals per line. Self-pollinated seed set and genome stability (number of copy number variants) were significantly affected by the interaction between both B. rapa and B. oleracea parental genotypes. We identified 13 putative meiosis gene candidates which were significantly associated with frequency of copy number variants and which contained putatively harmful mutations in meiosis gene haplotypes for further investigation. Our results support the hypothesis that allelic variants inherited from parental genotypes affect genome stability and fertility in resynthesized rapeseed.}",
issn = {2160-1836},
doi = {10.1093/g3journal/jkad136},
url = {https://doi.org/10.1093/g3journal/jkad136},
eprint = {https://academic.oup.com/g3journal/article-pdf/13/8/jkad136/51044695/jkad136.pdf},
}

C. Simanjuntak, T. Gaiser, H. Ahrends, A. Ceglar, M. Singh, F. Ewert, and A. Srivastava, "Impact of climate extreme events and their causality on maize yield in South Africa," Scientific Reports, vol. 13, 2023. doi:10.1038/s41598-023-38921-0
[BibTeX] [PDF]

@article{article,
author = {Simanjuntak, Christian and Gaiser, Thomas and Ahrends, Hella and Ceglar, Andrej and Singh, Manmeet and Ewert, Frank and Srivastava, Amit},
year = {2023},
month = {08},
pages = {},
title = {Impact of climate extreme events and their causality on maize yield in South Africa},
volume = {13},
journal = {Scientific Reports},
doi = {10.1038/s41598-023-38921-0},
url={https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10393995/}
}

F. Ewert, R. Baatz, and R. Finger, "Agroecology for a Sustainable Agriculture and Food System: From Local Solutions to Large-Scale Adoption," Annual Review of Resource Economics, vol. 15, iss. Volume 15, 2023, pp. 351-381, 2023. doi:10.1146/annurev-resource-102422-090105
[BibTeX] [PDF]

Agroecology is often considered as the ultimate and most comprehensive solution to the many challenges of the agricultural and food system, also referred to as the agri-food system. This review investigates to what extent agroecology can become the mainstream model for transforming agriculture toward more sustainable and resilient agri-food systems within the given economic and political context. We find that enhancing agroecology will require a fully integrated multiscale systems approach from farm to region to globe. The approach must consider relevant processes and relationships, actors and stakeholders as well as drivers, sustainability indicators, and the respective assessment methods across all scales. Giving specific attention to drivers related to economy, technology, and policy we point out that agroecology needs to be economically viable for farmers and other food system actors. In particular, new and emerging technologies related to digitalization and breeding should be given more consideration in agroecological transformation. We stress the need for an analytical and operational framework and adequate multiscale policy design and suggest six areas of needed attention to support the large-scale adoption of agroecology.

@article{annurev:/content/journals/10.1146/annurev-resource-102422-090105,
author = "Ewert, Frank and Baatz, Roland and Finger, Robert",
title = "Agroecology for a Sustainable Agriculture and Food System: From Local Solutions to Large-Scale Adoption",
journal= "Annual Review of Resource Economics",
year = "2023",
volume = "15",
number = "Volume 15, 2023",
pages = "351-381",
doi = "10.1146/annurev-resource-102422-090105",
url = "https://www.annualreviews.org/content/journals/10.1146/annurev-resource-102422-090105",
publisher = "Annual Reviews",
issn = "1941-1359",
type = "Journal Article",
keywords = "multiactor approaches",
keywords = "JEL Q5",
keywords = "multiple scales",
keywords = "food policy",
keywords = "JEL Q1",
keywords = "agricultural transformation",
keywords = "JEL Q2",
keywords = "sustainable agriculture",
keywords = "emerging technologies",
keywords = "agricultural policy",
keywords = "food system",
abstract = "Agroecology is often considered as the ultimate and most comprehensive solution to the many challenges of the agricultural and food system, also referred to as the agri-food system. This review investigates to what extent agroecology can become the mainstream model for transforming agriculture toward more sustainable and resilient agri-food systems within the given economic and political context. We find that enhancing agroecology will require a fully integrated multiscale systems approach from farm to region to globe. The approach must consider relevant processes and relationships, actors and stakeholders as well as drivers, sustainability indicators, and the respective assessment methods across all scales. Giving specific attention to drivers related to economy, technology, and policy we point out that agroecology needs to be economically viable for farmers and other food system actors. In particular, new and emerging technologies related to digitalization and breeding should be given more consideration in agroecological transformation. We stress the need for an analytical and operational framework and adequate multiscale policy design and suggest six areas of needed attention to support the large-scale adoption of agroecology.",
}

H. Storm, T. Heckelei, K. Baylis, and K. Mittenzwei, "Identifying farmers' response to changes in marginal and average subsidies using deep learning," American Journal of Agricultural Economics, 2023. doi:10.1111/ajae.12442
[BibTeX] [PDF]

Abstract Much of the developed world has adopted substantial, complex agricultural subsidy schemes in an attempt to produce desired rural livelihood and environmental outcomes. Understanding how farmers adjust their production activity in response to farm subsidies is crucial for setting optimal agricultural policy. Whereas standard economic theory suggests that farmers largely adjust production levels in response to prices and marginal subsidy rates, recent work in consumer behavior suggests that average (dis-)incentives may play a relevant role. We use a unique panel covering all farms applying for subsidies in Norway and a flexible deep-learning method to exploit kinks in the subsidy scheme to answer whether farmers respond more to average or marginal subsidies. In contrast to the standard economic theory of production, we find suggestive empirical evidence that farmers respond more to changes in average payments than to changes in marginal payments. We anticipate that our findings on the relevance of average payment levels for farmers' decision making may inspire further theoretical and empirical inquiries into agricultural policy effects. The study also highlights how novel deep-learning tools can be applied for detailed policy analysis and what advantages and challenges come with it. We believe that this approach has substantial potential for analysts and policymakers to evaluate and predict the impacts of policy options.

@article{https://doi.org/10.1111/ajae.12442,
author = {Storm, Hugo and Heckelei, Thomas and Baylis, Kathy and Mittenzwei, Klaus},
title = {Identifying farmers' response to changes in marginal and average subsidies using deep learning},
journal = {American Journal of Agricultural Economics},
year={2023},
pages = {},
keywords = {farm activities, farm growth, farm subsidies, machine learning, recurrent neural network},
doi = {10.1111/ajae.12442},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/ajae.12442},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/ajae.12442},
abstract = {Abstract Much of the developed world has adopted substantial, complex agricultural subsidy schemes in an attempt to produce desired rural livelihood and environmental outcomes. Understanding how farmers adjust their production activity in response to farm subsidies is crucial for setting optimal agricultural policy. Whereas standard economic theory suggests that farmers largely adjust production levels in response to prices and marginal subsidy rates, recent work in consumer behavior suggests that average (dis-)incentives may play a relevant role. We use a unique panel covering all farms applying for subsidies in Norway and a flexible deep-learning method to exploit kinks in the subsidy scheme to answer whether farmers respond more to average or marginal subsidies. In contrast to the standard economic theory of production, we find suggestive empirical evidence that farmers respond more to changes in average payments than to changes in marginal payments. We anticipate that our findings on the relevance of average payment levels for farmers' decision making may inspire further theoretical and empirical inquiries into agricultural policy effects. The study also highlights how novel deep-learning tools can be applied for detailed policy analysis and what advantages and challenges come with it. We believe that this approach has substantial potential for analysts and policymakers to evaluate and predict the impacts of policy options.}
}

N. Okole, F. I. R. Yamati, R. Hossain, M. Varrelmann, A. Mahlein, and R. H. Heim, "Hyperspectral signatures and betalain indicator for beet mosaic virus infection in sugar beet," in 2023 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) , 2023, pp. 506-511. doi:10.1109/MetroAgriFor58484.2023.10424290
[BibTeX]

@INPROCEEDINGS{10424290,
author={Okole, Nathan and Yamati, Facundo R. Ispizua and Hossain, Roxana and Varrelmann, Mark and Mahlein, Anne-Katrin and Heim, Rene Hj},
booktitle={2023 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)},
title={Hyperspectral signatures and betalain indicator for beet mosaic virus infection in sugar beet},
year={2023},
volume={},
number={},
pages={506-511},
keywords={Spectroradiometers;Sugar industry;Sensors;Pigmentation;Viruses (medical);Immune system;Diseases;breeding;plant phenotyping;non-imaging spectroscopy;sugar beet},
doi={10.1109/MetroAgriFor58484.2023.10424290}}

F. I. R. Yamati, R. H. Heim, M. Günder, W. Gajda, and A. Mahlein, "Image-to-image translation for satellite and UAV remote sensing: a use case for Cercospora Leaf Spot monitoring on sugar beet.," in 2023 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) , 2023, pp. 783-787. doi:10.1109/MetroAgriFor58484.2023.10424276
[BibTeX]

@INPROCEEDINGS{10424276,
author={Yamati, Facundo R. Ispizua and Heim, Rene Hj and Günder, Maurice and Gajda, Weronika and Mahlein, Anne-Katrin},
booktitle={2023 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)},
title={Image-to-image translation for satellite and UAV remote sensing: a use case for Cercospora Leaf Spot monitoring on sugar beet.},
year={2023},
volume={},
number={},
pages={783-787},
keywords={Satellites;Machine learning;Forestry;Autonomous aerial vehicles;Sugar industry;Remote sensing;Sensor arrays;deep learning;drones;multispectral;plant pathology;scale},
doi={10.1109/MetroAgriFor58484.2023.10424276}}

E. Rezaei, H. Webber, S. Asseng, K. Boote, J. Durand, F. Ewert, P. Martre, and D. Maccarthy, "Climate change impacts on crop yields," Nature Reviews Earth & Environment, vol. 4, 2023. doi:10.1038/s43017-023-00491-0
[BibTeX]

@article{article,
author = {Rezaei, Ehsan and Webber, Heidi and Asseng, Senthold and Boote, Kenneth and Durand, Jean-Louis and Ewert, Frank and Martre, Pierre and Maccarthy, Dilys},
year = {2023},
month = {11},
pages = {},
title = {Climate change impacts on crop yields},
volume = {4},
journal = {Nature Reviews Earth & Environment},
doi = {10.1038/s43017-023-00491-0}
}

T. Medic, J. Bömer, and S. Paulus, "CHALLENGES AND RECOMMENDATIONS FOR 3D PLANT PHENOTYPING IN AGRICULTURE USING TERRESTRIAL LASERS SCANNERS," ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. X-1/W1-2023, p. 1007–1014, 2023. doi:10.5194/isprs-annals-X-1-W1-2023-1007-2023
[BibTeX] [PDF]

@Article{isprs-annals-X-1-W1-2023-1007-2023,
AUTHOR = {Medic, T. and B\"omer, J. and Paulus, S.},
TITLE = {CHALLENGES AND RECOMMENDATIONS FOR 3D PLANT PHENOTYPING IN AGRICULTURE USING TERRESTRIAL LASERS SCANNERS},
JOURNAL = {ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
VOLUME = {X-1/W1-2023},
YEAR = {2023},
PAGES = {1007--1014},
URL = {https://isprs-annals.copernicus.org/articles/X-1-W1-2023/1007/2023/},
DOI = {10.5194/isprs-annals-X-1-W1-2023-1007-2023}
}

M. Farag, J. Kierdorf, and R. Roscher, "Inductive Conformal Prediction for Harvest-Readiness Classification of Cauliflower Plants: A Comparative Study of Uncertainty Quantification Methods," in 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) , 2023, pp. 651-659. doi:10.1109/ICCVW60793.2023.00072
[BibTeX] [PDF]

@INPROCEEDINGS{10350846,
author={Farag, Mohamed and Kierdorf, Jana and Roscher, Ribana},
booktitle={2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)},
title={Inductive Conformal Prediction for Harvest-Readiness Classification of Cauliflower Plants: A Comparative Study of Uncertainty Quantification Methods},
year={2023},
volume={},
number={},
pages={651-659},
doi={10.1109/ICCVW60793.2023.00072},
url={https://openaccess.thecvf.com/content/ICCV2023W/CVPPA/papers/Farag_Inductive_Conformal_Prediction_for_Harvest-Readiness_Classification_of_Cauliflower_Plants_A_ICCVW_2023_paper.pdf
}}

J. Rückin, F. Magistri, C. Stachniss, and M. Popović, "An Informative Path Planning Framework for Active Learning in UAV-Based Semantic Mapping," IEEE Transactions on Robotics, vol. 39, iss. 6, pp. 4279-4296, 2023. doi:10.1109/TRO.2023.3313811
[BibTeX] [PDF] [Code] [Video]

@ARTICLE{10264196,
author={Rückin, Julius and Magistri, Federico and Stachniss, Cyrill and Popović, Marija},
journal={IEEE Transactions on Robotics},
title={An Informative Path Planning Framework for Active Learning in UAV-Based Semantic Mapping},
year={2023},
volume={39},
number={6},
pages={4279-4296},
codeurl={https://github.com/dmar-bonn/ipp-al-framework},
videourl={https://www.youtube.com/watch?v=qFBciGSM-Gs},
doi={10.1109/TRO.2023.3313811},
URL = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/rueckin2023tro.pdf},
}

E. Martinsson, H. Hansson, K. Mittenzwei, and H. Storm, "Evaluating environmental effects of adopting automatic milking systems on Norwegian dairy farms," European Review of Agricultural Economics, p. jbad041, 2023. doi:10.1093/erae/jbad041
[BibTeX] [PDF]

{We present a novel procedure based on eco-efficiency for assessing farm-level effects of technology adoption while considering secondary effects. Secondary effects are defined as structural and behavioural adaptations to technology that may impact environmental, social or economic outcomes. We apply the procedure to automatic milking systems (AMS) in Norway and find that AMS induces secondary effects, most strongly by decreasing labour per cow and increasing herd sizes. For estimating effects of AMS we employ a novel causal machine learning approach. AMS induce heterogenous effects on eco-efficiency, negatively associated with herd expansion and labour per cow.}

@article{10.1093/erae/jbad041,
author = {Martinsson, Elin and Hansson, Helena and Mittenzwei, Klaus and Storm, Hugo},
title = "{Evaluating environmental effects of adopting automatic milking systems on Norwegian dairy farms}",
journal = {European Review of Agricultural Economics},
pages = {jbad041},
year = {2023},
month = {12},
abstract = "{We present a novel procedure based on eco-efficiency for assessing farm-level effects of technology adoption while considering secondary effects. Secondary effects are defined as structural and behavioural adaptations to technology that may impact environmental, social or economic outcomes. We apply the procedure to automatic milking systems (AMS) in Norway and find that AMS induces secondary effects, most strongly by decreasing labour per cow and increasing herd sizes. For estimating effects of AMS we employ a novel causal machine learning approach. AMS induce heterogenous effects on eco-efficiency, negatively associated with herd expansion and labour per cow.}",
issn = {0165-1587},
doi = {10.1093/erae/jbad041},
url = {https://doi.org/10.1093/erae/jbad041},
eprint = {https://academic.oup.com/erae/advance-article-pdf/doi/10.1093/erae/jbad041/54424173/jbad041.pdf},
}

T. T. Stomberg, J. Leonhardt, I. Weber, and R. Roscher, "Recognizing protected and anthropogenic patterns in landscapes using interpretable machine learning and satellite imagery," Frontiers in Artificial Intelligence, vol. 6, 2023. doi:10.3389/frai.2023.1278118
[BibTeX] [PDF]

The accurate and comprehensive mapping of land cover has become a central task in modern environmental research, with increasing emphasis on machine learning approaches. However, a clear technical definition of the land cover class is a prerequisite for learning and applying a machine learning model. One of the challenging classes is naturalness and human influence, yet mapping it is important due to its critical role in biodiversity conservation, habitat assessment, and climate change monitoring. We present an interpretable machine learning approach to map patterns related to territorial protected and anthropogenic areas as proxies of naturalness and human influence using satellite imagery. To achieve this, we train a weakly-supervised convolutional neural network and subsequently apply attribution methods such as Grad-CAM and occlusion sensitivity mapping. We propose a novel network architecture that consists of an image-to-image network and a shallow, task-specific head. Both sub-networks are connected by an intermediate layer that captures high-level features in full resolution, allowing for detailed analysis with a wide range of attribution methods. We further analyze how intermediate layer activations relate to their attributions across the training dataset to establish a consistent relationship. This makes attributions consistent across different scenes and allows for a large-scale analysis of remote sensing data. The results highlight that our approach is a promising way to observe and assess naturalness and territorial protection.

@ARTICLE{10.3389/frai.2023.1278118,
AUTHOR={Stomberg, Timo T. and Leonhardt, Johannes and Weber, Immanuel and Roscher, Ribana},
TITLE={Recognizing protected and anthropogenic patterns in landscapes using interpretable machine learning and satellite imagery},
JOURNAL={Frontiers in Artificial Intelligence},
VOLUME={6},
YEAR={2023},
URL={https://www.frontiersin.org/articles/10.3389/frai.2023.1278118},
DOI={10.3389/frai.2023.1278118},
ISSN={2624-8212},
ABSTRACT={The accurate and comprehensive mapping of land cover has become a central task in modern environmental research, with increasing emphasis on machine learning approaches. However, a clear technical definition of the land cover class is a prerequisite for learning and applying a machine learning model. One of the challenging classes is naturalness and human influence, yet mapping it is important due to its critical role in biodiversity conservation, habitat assessment, and climate change monitoring. We present an interpretable machine learning approach to map patterns related to territorial protected and anthropogenic areas as proxies of naturalness and human influence using satellite imagery. To achieve this, we train a weakly-supervised convolutional neural network and subsequently apply attribution methods such as Grad-CAM and occlusion sensitivity mapping. We propose a novel network architecture that consists of an image-to-image network and a shallow, task-specific head. Both sub-networks are connected by an intermediate layer that captures high-level features in full resolution, allowing for detailed analysis with a wide range of attribution methods. We further analyze how intermediate layer activations relate to their attributions across the training dataset to establish a consistent relationship. This makes attributions consistent across different scenes and allows for a large-scale analysis of remote sensing data. The results highlight that our approach is a promising way to observe and assess naturalness and territorial protection.}
}

A. Roychoudhury, S. Khorshidi, S. Agrawal, and M. Bennewitz, "Perception for Humanoid Robots," Current Robotics Reports - Springer Nature, 2023. doi:10.1007/s43154-023-00107-x
[BibTeX] [PDF]

@article{RoychoudhuryCurrentRoboticReports2023,
title = {Perception for Humanoid Robots},
journal = {Current Robotics Reports - Springer Nature},
year = {2023},
author = {Roychoudhury, Arindam and Khorshidi, Shahram and Agrawal, Subham and Bennewitz, Maren},
url = {https://doi.org/10.1007/s43154-023-00107-x},
publisher = {Springer Nature},
doi = {10.1007/s43154-023-00107-x}}

A. Barreto, L. Reifenrath, R. Vogg, F. Sinz, and A. Mahlein, "Data Augmentation for Mask-Based Leaf Segmentation of UAV-Images as a Basis to Extract Leaf-Based Phenotyping Parameters," KI - Künstliche Intelligenz, vol. 37, 2023. doi:10.1007/s13218-023-00815-8
[BibTeX]

@article{article,
author = {Barreto, Abel and Reifenrath, Lasse and Vogg, Richard and Sinz, Fabian and Mahlein, Anne-Katrin},
year = {2023},
month = {11},
pages = {},
title = {Data Augmentation for Mask-Based Leaf Segmentation of UAV-Images as a Basis to Extract Leaf-Based Phenotyping Parameters},
volume = {37},
journal = {KI - Künstliche Intelligenz},
doi = {10.1007/s13218-023-00815-8}
}

V. Sushko, R. Wang, and J. Gall, "Smoothness Similarity Regularization for Few-Shot GAN Adaptation," in International Conference on Computer Vision (ICCV) , 2023.
[BibTeX] [PDF]

@inproceedings{SushkoICCV2023,
title={Smoothness Similarity Regularization for Few-Shot GAN Adaptation},
year = {2023},
author={Vadim Sushko and Ruyu Wang and Juergen Gall},
booktitle = {International Conference on Computer Vision (ICCV)},
url = {https://openaccess.thecvf.com/content/ICCV2023/papers/Sushko_Smoothness_Similarity_Regularization_for_Few-Shot_GAN_Adaptation_ICCV_2023_paper.pdf}}

J. Leonhardt, L. Drees, J. Gall, and R. Roscher, "Leveraging Bioclimatic Context for Supervised and Self-Supervised Land Cover Classification," in DAGM German Conference on Pattern Recognition , 2023.
[BibTeX] [PDF] [Code]

@inproceedings{LeonhardtDAGM23,
title={Leveraging Bioclimatic Context for Supervised and Self-Supervised Land Cover Classification},
year = {2023},
author={Johannes Leonhardt and Lukas Drees and Jürgen Gall and Ribana Roscher},
booktitle = {DAGM German Conference on Pattern Recognition},
codeurl = {https://github.com/johannes-leonhardt/leveraging-bioclimatic-context-for-land-cover-classification-public},
url={https://pages.iai.uni-bonn.de/gall_juergen/download/jgall_bioclimaticcontext_gcpr23.pdf}}

E. Agathokleous, M. Frei, O. M. Knopf, O. Muller, Y. Xu, T. H. Nguyen, T. Gaiser, X. Liu, B. Liu, C. J. Saitanis, B. Shang, M. S. Alam, Y. Feng, F. Ewert, and Z. Feng, "Adapting crop production to climate change and air pollution at different scales," Nature Food, 2023. doi:10.1038/s43016-023-00858-y
[BibTeX]

Air pollution and climate change are tightly interconnected and jointly affect field crop production and agroecosystem health. Although our understanding of the individual and combined impacts of air pollution and climate change factors is improving, the adaptation of crop production to concurrent air pollution and climate change remains challenging to resolve. Here we evaluate recent advances in the adaptation of crop production to climate change and air pollution at the plant, field and ecosystem scales. The main approaches at the plant level include the integration of genetic variation, molecular breeding and phenotyping. Field-level techniques include optimizing cultivation practices, promoting mixed cropping and diversification, and applying technologies such as antiozonants, nanotechnology and robot-assisted farming. Plant- and field-level techniques would be further facilitated by enhancing soil resilience, incorporating precision agriculture and modifying the hydrology and microclimate of agricultural landscapes at the ecosystem level. Strategies and opportunities for crop production under climate change and air pollution are discussed.

@article{Agathokleous2023,
author = {Agathokleous, Evgenios and Frei, Michael and Knopf, Oliver M. and Muller, Onno and Xu, Yansen and Nguyen, Thuy Huu and Gaiser, Thomas and Liu, Xiaoyu and Liu, Bing and Saitanis, Costas J. and Shang, Bo and Alam, Muhammad Shahedul and Feng, Yanru and Ewert, Frank and Feng, Zhaozhong},
year = {2023},
title = {Adapting crop production to climate change and air pollution at different scales},
journal = {Nature Food},
abstract = {Air pollution and climate change are tightly interconnected and jointly affect field crop production and agroecosystem health. Although our understanding of the individual and combined impacts of air pollution and climate change factors is improving, the adaptation of crop production to concurrent air pollution and climate change remains challenging to resolve. Here we evaluate recent advances in the adaptation of crop production to climate change and air pollution at the plant, field and ecosystem scales. The main approaches at the plant level include the integration of genetic variation, molecular breeding and phenotyping. Field-level techniques include optimizing cultivation practices, promoting mixed cropping and diversification, and applying technologies such as antiozonants, nanotechnology and robot-assisted farming. Plant- and field-level techniques would be further facilitated by enhancing soil resilience, incorporating precision agriculture and modifying the hydrology and microclimate of agricultural landscapes at the ecosystem level. Strategies and opportunities for crop production under climate change and air pollution are discussed.},
doi = {10.1038/s43016-023-00858-y}
}

D. Schulz and J. Börner, "No impact of repeated digital advisory service to Haitian peanut producers," Q Open, p. qoad023, 2023. doi:10.1093/qopen/qoad023
[BibTeX] [PDF]

{Digital farm advisory services can be a cost-effective way to provide relevant information to smallholders in developing countries. Information provision has been shown to generate positive impacts on agricultural practices and farmer's income across various settings. We conducted a pre-registered randomized control trial among peanut farmers in Haiti to evaluate the impact of short text messages. We administered two waves of digital information provision and follow-up surveys. Results suggest no measurable impact of digital information delivery on agricultural knowledge, practice adoption or productivity. We discuss internal and external validity of these findings and derive recommendations for future interventions.}

@article{10.1093/qopen/qoad023,
author = {Schulz, Dario and Börner, Jan},
title = {No impact of repeated digital advisory service to Haitian peanut producers},
journal = {Q Open},
pages = {qoad023},
year = {2023},
month = {09},
abstract = "{Digital farm advisory services can be a cost-effective way to provide relevant information to smallholders in developing countries. Information provision has been shown to generate positive impacts on agricultural practices and farmer's income across various settings. We conducted a pre-registered randomized control trial among peanut farmers in Haiti to evaluate the impact of short text messages. We administered two waves of digital information provision and follow-up surveys. Results suggest no measurable impact of digital information delivery on agricultural knowledge, practice adoption or productivity. We discuss internal and external validity of these findings and derive recommendations for future interventions.}",
issn = {2633-9048},
doi = {10.1093/qopen/qoad023},
url = {https://doi.org/10.1093/qopen/qoad023},
eprint = {https://academic.oup.com/qopen/advance-article-pdf/doi/10.1093/qopen/qoad023/51792545/qoad023.pdf},
}

G. Roggiolani, F. Magistri, T. Guadagnino, J. Behley, and C. Stachniss, "Unsupervised Pre-Training for 3D Leaf Instance Segmentation," IEEE Robotics and Automation Letters, vol. 8, iss. 11, pp. 7448-7455, 2023. doi:10.1109/LRA.2023.3320018
[BibTeX] [PDF] [Code] [Video]

@ARTICLE{10265122,
author={Roggiolani, Gianmarco and Magistri, Federico and Guadagnino, Tiziano and Behley, Jens and Stachniss, Cyrill},
journal={IEEE Robotics and Automation Letters},
title={Unsupervised Pre-Training for 3D Leaf Instance Segmentation},
year={2023},
volume={8},
number={11},
pages={7448-7455},
doi={10.1109/LRA.2023.3320018},
codeurl={https://github.com/PRBonn/Unsupervised-Pre-Training-for-3D-Leaf-Instance-Segmentation},
videourl={https://www.youtube.com/watch?v=PbYVPPwVeKg},
url={https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/roggiolani2023ral.pdf}}

C. Hubert, K. Luhmer, M. Moll, and R. Pude, "Einfluss von Stickstoff und Zink auf den Gehalt an ätherischen Ölen in verschiedenen Mentha-Genotypen," in Mitt. Ges. Pflanzenbauwiss. , 2023, p. 227–228.
[BibTeX] [PDF]

@inproceedings{hubertmenthapflanzenbau,
author = {Hubert, Charlotte and Luhmer, Katharina and Moll, Marcel and Pude, Ralf},
year = {2023},
month = {09},
title = {Einfluss von Stickstoff und Zink auf den Gehalt an ätherischen Ölen in verschiedenen Mentha-Genotypen},
booktitle = {Mitt. Ges. Pflanzenbauwiss.},
volume = {33},
pages = {227–228},
issn ={ISSN 0934-5116},
url = {https://www.researchgate.net/publication/374169369_Einfluss_von_Stickstoff_und_Zink_auf_den_Gehalt_an_atherischen_Olen_in_verschiedenen_Mentha-Genotypen}
}

M. Cantürk, L. Zabawa, D. Pavlic, A. Dreier, L. Klingbeil, and H. Kuhlmann, "UAV-based individual plant detection and geometric parameter extraction in vineyards," Frontiers in Plant Science, vol. 14, 2023. doi:10.3389/fpls.2023.1244384
[BibTeX] [PDF]

Accurately characterizing vineyard parameters is crucial for precise vineyard management and breeding purposes. Various macroscopic vineyard parameters are required to make informed management decisions, such as pesticide application, defoliation strategies, and determining optimal sugar content in each berry by assessing biomass. In this paper, we present a novel approach that utilizes point cloud data to detect trunk positions and extract macroscopic vineyard characteristics, including plant height, canopy width, and canopy volume. Our approach relies solely on geometric features and is compatible with different training systems and data collected using various 3D sensors. To evaluate the effectiveness and robustness of our proposed approach, we conducted extensive experiments on multiple grapevine rows trained in two different systems. Our method provides more comprehensive canopy characteristics than traditional manual measurements, which are not representative throughout the row. The experimental results demonstrate the accuracy and efficiency of our method in extracting vital macroscopic vineyard characteristics, providing valuable insights for yield monitoring, grape quality optimization, and strategic interventions to enhance vineyard productivity and sustainability.

@ARTICLE{10.3389/fpls.2023.1244384,
AUTHOR={Cantürk, Meltem and Zabawa, Laura and Pavlic, Diana and Dreier, Ansgar and Klingbeil, Lasse and Kuhlmann, Heiner },
TITLE={UAV-based individual plant detection and geometric parameter extraction in vineyards},
JOURNAL={Frontiers in Plant Science},
VOLUME={14},
YEAR={2023},
URL={https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2023.1244384},
DOI={10.3389/fpls.2023.1244384},
ISSN={1664-462X},
ABSTRACT={Accurately characterizing vineyard parameters is crucial for precise vineyard management and breeding purposes. Various macroscopic vineyard parameters are required to make informed management decisions, such as pesticide application, defoliation strategies, and determining optimal sugar content in each berry by assessing biomass. In this paper, we present a novel approach that utilizes point cloud data to detect trunk positions and extract macroscopic vineyard characteristics, including plant height, canopy width, and canopy volume. Our approach relies solely on geometric features and is compatible with different training systems and data collected using various 3D sensors. To evaluate the effectiveness and robustness of our proposed approach, we conducted extensive experiments on multiple grapevine rows trained in two different systems. Our method provides more comprehensive canopy characteristics than traditional manual measurements, which are not representative throughout the row. The experimental results demonstrate the accuracy and efficiency of our method in extracting vital macroscopic vineyard characteristics, providing valuable insights for yield monitoring, grape quality optimization, and strategic interventions to enhance vineyard productivity and sustainability.
}}

C. Hubert, S. Bartoschek, K. Luhmer, M. D. Moll, and R. Pude, "Einfluss einer Mykorrhizierung auf den ätherischen Ölgehalt und die physiologische Reaktion von Mentha-Genotypen unter verschiedenen UV-Behandlungen," 9. Tagung für Arznei- und Gewürzpflanzenforschung : Sicherheit vom Anbau bis zum Verbraucher – Spitzenklasse oder auf die Spitze getrieben? ; Freising, 11. - 14. September 2023 ; Kurzfassungen der Vorträge und Poster, vol. 476, p. 44–48, 2023. doi:10.5073/20230821-132157-0
[BibTeX] [PDF]

Mentha sp. dient sowohl zur Teeproduktion als auch zur Gewinnung von ätherischen Ölen. Ätherische Öle sind aufgrund von aromatischen und gesundheitsfördernden Eigenschaften Bestandteil vieler Produkte der Lebensmittel-, Kosmetik- und Pharmaindustrie. Ein möglichst hoher ätherischer Ölgehalt in der Pflanze ist somit wünschenswert. Deswegen befasst sich diese Studie mit dem Gehalt an ätherischem Öl und der physiologischen Reaktion von verschiedenen Mentha Genotypen zum einen unter unterschiedlichen Lichtbedingungen und zum anderen unter Einfluss von einer Mykorrhizierung. Dazu wurden drei verschiedene Mentha Genotypen (Mentha {\texttimes} piperita 'Multimentha', Mentha {\texttimes} piperita 'Fränkische Blaue' und Mentha rotundifolia 'Apfelminze') unterschiedlichen UV-Behandlungen ausgesetzt (Kontrolle (Einstrahlung unter Gewächshausbedingugen), erhöhte UV-B-Strahlung (1,4 W/m{\texttwosuperior}, 700 {\%} der Kontrolle) und schattierte Bedingungen (30 {\%} der Kontrolle)) und teilweise mit arbuskulärer Mykorrhiza geimpft. Die Pflanzenvitalität wurde mithilfe von Vegetationsindizes (VIs) ermittelt. Unabhängig der Lichtbedingungen wurde vor allem die 'Multimentha' durch die Mykorrhizierung beeinflusst. Sie wies signifikant höhere Werte für Pflanzenhöhe, Trockenmasse und ätherischen Ölgehalt als die 'Apfelminze' und 'Fränkische Blaue' auf. Zusammenfassend kann gesagt werden, dass sich eine Mykorrhizierung nur auf einzelne Genotypen positiv auswirkt und somit eine weitere Auswahl von Genotypen betrachtet werden sollte.

@Article{openagrar_mods_00089191,
author = {Hubert, Charlotte and Bartoschek, Sonja and Luhmer, Katharina and Moll, Marcel Dieter and Pude, Ralf},
editor = {Henning, Volker and Heuberger, Heidi and Marthe, Frank},
title = {Einfluss einer Mykorrhizierung auf den {\"a}therischen {\"O}lgehalt und die physiologische Reaktion von Mentha-Genotypen unter verschiedenen UV-Behandlungen},
journal = {9. Tagung f{\"u}r Arznei- und Gew{\"u}rzpflanzenforschung : Sicherheit vom Anbau bis zum Verbraucher -- Spitzenklasse oder auf die Spitze getrieben? ; Freising, 11. - 14. September 2023 ; Kurzfassungen der Vortr{\"a}ge und Poster},
year = {2023},
month = {Sep},
day = {14},
publisher = {Julius K{\"u}hn-Institute},
address = {Quedlinburg},
volume = {476},
pages = {44--48},
keywords = {Mentha {\texttimes} piperita; Mentha rotundifolia; UV-Strahlung; Mykorrhiza; UV-radiation; mycorrhiza},
abstract = {Mentha sp. dient sowohl zur Teeproduktion als auch zur Gewinnung von {\"a}therischen {\"O}len. {\"A}therische {\"O}le sind aufgrund von aromatischen und gesundheitsf{\"o}rdernden Eigenschaften Bestandteil vieler Produkte der Lebensmittel-, Kosmetik- und Pharmaindustrie. Ein m{\"o}glichst hoher {\"a}therischer {\"O}lgehalt in der Pflanze ist somit w{\"u}nschenswert. Deswegen befasst sich diese Studie mit dem Gehalt an {\"a}therischem {\"O}l und der physiologischen Reaktion von verschiedenen Mentha Genotypen zum einen unter unterschiedlichen Lichtbedingungen und zum anderen unter Einfluss von einer Mykorrhizierung. Dazu wurden drei verschiedene Mentha Genotypen (Mentha {\texttimes} piperita 'Multimentha', Mentha {\texttimes} piperita 'Fr{\"a}nkische Blaue' und Mentha rotundifolia 'Apfelminze') unterschiedlichen UV-Behandlungen ausgesetzt (Kontrolle (Einstrahlung unter Gew{\"a}chshausbedingugen), erh{\"o}hte UV-B-Strahlung (1,4 W/m{\texttwosuperior}, 700 {\%} der Kontrolle) und schattierte Bedingungen (30 {\%} der Kontrolle)) und teilweise mit arbuskul{\"a}rer Mykorrhiza geimpft. Die Pflanzenvitalit{\"a}t wurde mithilfe von Vegetationsindizes (VIs) ermittelt. Unabh{\"a}ngig der Lichtbedingungen wurde vor allem die 'Multimentha' durch die Mykorrhizierung beeinflusst. Sie wies signifikant h{\"o}here Werte f{\"u}r Pflanzenh{\"o}he, Trockenmasse und {\"a}therischen {\"O}lgehalt als die 'Apfelminze' und 'Fr{\"a}nkische Blaue' auf. Zusammenfassend kann gesagt werden, dass sich eine Mykorrhizierung nur auf einzelne Genotypen positiv auswirkt und somit eine weitere Auswahl von Genotypen betrachtet werden sollte.},
isbn = {978-3-95547-134-7},
issn = {1868-9892},
doi = {10.5073/20230821-132157-0},
url = {https://www.openagrar.de/receive/openagrar_mods_00089191},
url = {https://doi.org/10.5073/20230821-132157-0},
url = {https://doi.org/10.5073/20230821-142701-0},
file = {:https://www.openagrar.de/servlets/MCRFileNodeServlet/openagrar_derivate_00054523/JKA_476_2023_8.pdf:PDF},
language = {en}
}

Y. Wang, S. Schaub, D. Wuepper, and R. Finger, "Culture and agricultural biodiversity conservation," Food Policy, vol. 120, p. 102482, 2023. doi:10.1016/j.foodpol.2023.102482
[BibTeX] [PDF]

Farmers’ behavior towards sustainable agricultural production is key to reducing the environmental footprint of agriculture and conserving biodiversity. We investigate the causal effect of culture on pro-environmental behaviors of farmers, and how policy instruments interact with culture to influence behavior. We exploit a unique natural experiment in Switzerland, which consists of two parts. First, there is an inner-Swiss cultural border between German- and French-speaking farmers who share the same natural environment, economy, and institutions, but differ culturally in their norms and values. Second, we exploit the effects of an agri-environmental policy reform that increased the monetary incentives to enroll land into biodiversity conservation. Using a spatial difference-in-discontinuities design and panel census data of all Swiss farms between 2010 and 2017, we show the following findings: Before the reform, farmers on the French-speaking side of the cultural border systematically enrolled less land into biodiversity conservation, compared to the German-speaking side. With increased monetary incentives following the policy reform in 2014, the French-speaking farmers enrolled relatively more additional land than the German-speaking farmers, shrinking the discontinuity. These findings indicate that while there exist cultural differences in pro-environmental behaviors, increased monetary incentives can reduce the importance of cultural differences. We discuss the implications for policy.

@article{WANG2023102482,
title = {Culture and agricultural biodiversity conservation},
journal = {Food Policy},
volume = {120},
pages = {102482},
year = {2023},
issn = {0306-9192},
doi = {10.1016/j.foodpol.2023.102482},
url = {https://www.sciencedirect.com/science/article/pii/S0306919223000805},
author = {Yanbing Wang and Sergei Schaub and David Wuepper and Robert Finger},
keywords = {Biodiversity, Conservation, Culture and policy, Environmental behavior, Agri-environmental schemes, Result-based schemes},
abstract = {Farmers’ behavior towards sustainable agricultural production is key to reducing the environmental footprint of agriculture and conserving biodiversity. We investigate the causal effect of culture on pro-environmental behaviors of farmers, and how policy instruments interact with culture to influence behavior. We exploit a unique natural experiment in Switzerland, which consists of two parts. First, there is an inner-Swiss cultural border between German- and French-speaking farmers who share the same natural environment, economy, and institutions, but differ culturally in their norms and values. Second, we exploit the effects of an agri-environmental policy reform that increased the monetary incentives to enroll land into biodiversity conservation. Using a spatial difference-in-discontinuities design and panel census data of all Swiss farms between 2010 and 2017, we show the following findings: Before the reform, farmers on the French-speaking side of the cultural border systematically enrolled less land into biodiversity conservation, compared to the German-speaking side. With increased monetary incentives following the policy reform in 2014, the French-speaking farmers enrolled relatively more additional land than the German-speaking farmers, shrinking the discontinuity. These findings indicate that while there exist cultural differences in pro-environmental behaviors, increased monetary incentives can reduce the importance of cultural differences. We discuss the implications for policy.}
}

D. Wuepper, H. Wang, W. Schlenker, M. Jain, and R. Finger, "Institutions and Global Crop Yields," National Bureau of Economic Research, Working Paper 31426, 2023. doi:10.3386/w31426
[BibTeX] [PDF] [Video]

We estimate annual discontinuities in remotely-sensed crop yields at all international land borders and link them to changes in the economic freedom index by the Fraser Institute, a country-level measure of institutional quality. Each point of the ten-point index increases the discontinuity by 2.2% over the next five years, highlighting that institutional reforms have the potential to close some of the observed crop yield gap. Three subcategories are consistently significant: credit market regulation, inflation, and the top marginal tax rate. We present suggestive evidence that higher average yields are achieved through increased use of irrigation and mechanization. Yield variability remains unchanged, and reforms lead to cropland expansion through deforestation.

@techreport{NBERw31426,
title = "Institutions and Global Crop Yields",
author = "Wuepper, David and Wang, Haoyu and Schlenker, Wolfram and Jain, Meha and Finger, Robert",
institution = "National Bureau of Economic Research",
type = "Working Paper",
series = "Working Paper Series",
number = "31426",
year = "2023",
month = "July",
doi = {10.3386/w31426},
videourl = {https://www.youtube.com/watch?v=pCNA0fpokuI},
URL = "http://www.nber.org/papers/w31426",
abstract = {We estimate annual discontinuities in remotely-sensed crop yields at all international land borders and link them to changes in the economic freedom index by the Fraser Institute, a country-level measure of institutional quality. Each point of the ten-point index increases the discontinuity by 2.2% over the next five years, highlighting that institutional reforms have the potential to close some of the observed crop yield gap. Three subcategories are consistently significant: credit market regulation, inflation, and the top marginal tax rate. We present suggestive evidence that higher average yields are achieved through increased use of irrigation and mechanization. Yield variability remains unchanged, and reforms lead to cropland expansion through deforestation.},
}

J. A. Tanke, O. Kwon, F. B. Mueller, A. Doering, and J. Gall, "Humans in Kitchens: A Dataset for Multi-Person Human Motion Forecasting with Scene Context," in Advances in Neural Information Processing Systems (NeurIPS 2023) , 2023.
[BibTeX] [PDF]

@INPROCEEDINGS{tankehumans,
author={Julian Alexander Tanke and Oh-Hun Kwon and Felix Benjamin Mueller and Andreas Doering and Juergen Gall},
booktitle={Advances in Neural Information Processing Systems (NeurIPS 2023)},
title={Humans in Kitchens: A Dataset for Multi-Person Human Motion Forecasting with Scene Context},
year={2023},
url={https://proceedings.neurips.cc/paper_files/paper/2023/file/2052b3e0617ecb2ce9474a6feaf422b3-Paper-Datasets_and_Benchmarks.pdf},
}

O. Kwon and E. Zell, "Image-Coupled Volume Propagation for Stereo Matching," in 2023 IEEE International Conference on Image Processing (ICIP) , 2023, pp. 2510-2514. doi:10.1109/ICIP49359.2023.10222247
[BibTeX] [PDF] [Code] [Video]

@INPROCEEDINGS{10222247,
author={Kwon, Oh-Hun and Zell, Eduard},
booktitle={2023 IEEE International Conference on Image Processing (ICIP)},
title={Image-Coupled Volume Propagation for Stereo Matching},
year={2023},
volume={},
number={},
pages={2510-2514},
codeurl={https://github.com/ohkwon718/icvp},
videourl={https://www.youtube.com/watch?v=dVzmLc-ub54},
doi={10.1109/ICIP49359.2023.10222247},
url={https://arxiv.org/pdf/2301.00695},}

A. Dreier, B. Jost, H. Kuhlmann, and L. Klingbeil, "Investigations of the scan characteristics with special focus on multi-target capability for the 2D laser scanner RIEGL miniVUX-2UAV," Journal of Applied Geodesy, 2023. doi:10.1515/jag-2022-0029
[BibTeX]

@article{DreierJostKuhlmannKlingbeil+2023,
title = {Investigations of the scan characteristics with special focus on multi-target capability for the 2D laser scanner RIEGL miniVUX-2UAV},
author = {Ansgar Dreier and Berit Jost and Heiner Kuhlmann and Lasse Klingbeil},
journal = {Journal of Applied Geodesy},
doi = {10.1515/jag-2022-0029},
year = {2023},
lastchecked = {2023-10-04},
}

A. Dreier, H. Kuhlmann, and L. Klingbeil, "Quality Investigation of UAV-Based Laser Scanning with Detailed Study of Multi-Target Capability," in Ingenieurvermessung 23, Beiträge zum 20. Internationalen Ingenieurvermessungskurs , 2023.
[BibTeX] [PDF]

@inproceedings{dreieringenieuervermessungskurs2023,
author = {Dreier, Ansgar and Kuhlmann, Heiner and Klingbeil, Lasse},
year = {2023},
month = {04},
publisher = {Wichmann Verlag},
booktitle={Ingenieurvermessung 23, Beiträge zum 20. Internationalen Ingenieurvermessungskurs},
title = {Quality Investigation of UAV-Based Laser Scanning with Detailed Study of Multi-Target Capability},
URL = {https://www.researchgate.net/publication/372769814_Quality_Investigation_of_UAV-Based_Laser_Scanning_with_Detailed_Study_of_Multi-Target_Capability},
}

Y. Müller, P. Patwari, T. Stöcker, V. Zeisler-Diehl, U. Steiner, C. Campoli, L. Grewe, M. Kuczkowska, M. M. Dierig, S. Jose, A. M. Hetherington, I. F. Acosta, H. Schoof, L. Schreiber, and P. Dörmann, "Isolation and characterization of the gene HvFAR1 encoding acyl-CoA reductase from the cer-za.227 mutant of barley (Hordeum vulgare) and analysis of the cuticular barrier functions," New Phytologist, vol. 239, iss. 5, pp. 1903-1918, 2023. doi:10.1111/nph.19063
[BibTeX] [PDF]

Summary The cuticle is a protective layer covering aerial plant organs. We studied the function of waxes for the establishment of the cuticular barrier in barley (Hordeum vulgare). The barley eceriferum mutants cer-za.227 and cer-ye.267 display reduced wax loads, but the genes affected, and the consequences of the wax changes for the barrier function remained unknown. Cuticular waxes and permeabilities were measured in cer-za.227 and cer-ye.267. The mutant loci were isolated by bulked segregant RNA sequencing. New cer-za alleles were generated by genome editing. The CER-ZA protein was characterized after expression in yeast and Arabidopsis cer4-3. Cer-za.227 carries a mutation in HORVU5Hr1G089230 encoding acyl-CoA reductase (FAR1). The cer-ye.267 mutation is located to HORVU4Hr1G063420 encoding β-ketoacyl-CoA synthase (KAS1) and is allelic to cer-zh.54. The amounts of intracuticular waxes were strongly decreased in cer-ye.267. The cuticular water loss and permeability of cer-za.227 were similar to wild-type (WT), but were increased in cer-ye.267. Removal of epicuticular waxes revealed that intracuticular, but not epicuticular waxes are required to regulate cuticular transpiration. The differential decrease in intracuticular waxes between cer-za.227 and cer-ye.267, and the removal of epicuticular waxes indicate that the cuticular barrier function mostly depends on the presence of intracuticular waxes.

@article{https://doi.org/10.1111/nph.19063,
author = {Müller, Yannic and Patwari, Payal and Stöcker, Tyll and Zeisler-Diehl, Viktoria and Steiner, Ulrike and Campoli, Chiara and Grewe, Lea and Kuczkowska, Magdalena and Dierig, Maya Marita and Jose, Sarah and Hetherington, Alistair M. and Acosta, Ivan F. and Schoof, Heiko and Schreiber, Lukas and Dörmann, Peter},
title = {Isolation and characterization of the gene HvFAR1 encoding acyl-CoA reductase from the cer-za.227 mutant of barley (Hordeum vulgare) and analysis of the cuticular barrier functions},
journal = {New Phytologist},
volume = {239},
number = {5},
pages = {1903-1918},
keywords = {barley, cuticle, cutin, eceriferum, permeability, wax},
doi = {10.1111/nph.19063},
url = {https://nph.onlinelibrary.wiley.com/doi/abs/10.1111/nph.19063},
eprint = {https://nph.onlinelibrary.wiley.com/doi/pdf/10.1111/nph.19063},
abstract = {Summary The cuticle is a protective layer covering aerial plant organs. We studied the function of waxes for the establishment of the cuticular barrier in barley (Hordeum vulgare). The barley eceriferum mutants cer-za.227 and cer-ye.267 display reduced wax loads, but the genes affected, and the consequences of the wax changes for the barrier function remained unknown. Cuticular waxes and permeabilities were measured in cer-za.227 and cer-ye.267. The mutant loci were isolated by bulked segregant RNA sequencing. New cer-za alleles were generated by genome editing. The CER-ZA protein was characterized after expression in yeast and Arabidopsis cer4-3. Cer-za.227 carries a mutation in HORVU5Hr1G089230 encoding acyl-CoA reductase (FAR1). The cer-ye.267 mutation is located to HORVU4Hr1G063420 encoding β-ketoacyl-CoA synthase (KAS1) and is allelic to cer-zh.54. The amounts of intracuticular waxes were strongly decreased in cer-ye.267. The cuticular water loss and permeability of cer-za.227 were similar to wild-type (WT), but were increased in cer-ye.267. Removal of epicuticular waxes revealed that intracuticular, but not epicuticular waxes are required to regulate cuticular transpiration. The differential decrease in intracuticular waxes between cer-za.227 and cer-ye.267, and the removal of epicuticular waxes indicate that the cuticular barrier function mostly depends on the presence of intracuticular waxes.},
year = {2023}
}

S. Paulus and B. Leiding, "Can Distributed Ledgers Help to Overcome the Need of Labeled Data for Agricultural Machine Learning Tasks?," Plant Phenomics, vol. 5, 2023. doi:10.34133/plantphenomics.0070
[BibTeX] [PDF]

@article{doi:10.34133/plantphenomics.0070,
author = {Paulus, Stefan and Leiding, Benjamin},
title = {Can Distributed Ledgers Help to Overcome the Need of Labeled Data for Agricultural Machine Learning Tasks?},
journal = {Plant Phenomics},
volume = {5},
year = {2023},
doi = {10.34133/plantphenomics.0070},
URL = {https://spj.science.org/doi/abs/10.34133/plantphenomics.0070}}

A. Barreto, F. R. Ispizua Yamati, M. Varrelmann, S. Paulus, and A. Mahlein, "Disease Incidence and Severity of Cercospora Leaf Spot in Sugar Beet Assessed by Multispectral Unmanned Aerial Images and Machine Learning," Plant Disease, vol. 107, iss. 1, pp. 188-200, 2023. doi:10.1094/PDIS-12-21-2734-RE
[BibTeX] [PDF]

Disease incidence (DI) and metrics of disease severity are relevant parameters for decision making in plant protection and plant breeding. To develop automated and sensor-based routines, a sugar beet variety trial was inoculated with Cercospora beticola and monitored with a multispectral camera system mounted to an unmanned aerial vehicle (UAV) over the vegetation period. A pipeline based on machine learning methods was established for image data analysis and extraction of disease-relevant parameters. Features based on the digital surface model, vegetation indices, shadow condition, and image resolution improved classification performance in comparison with using single multispectral channels in 12 and 6\% of diseased and soil regions, respectively. With a postprocessing step, area-related parameters were computed after classification. Results of this pipeline also included extraction of DI and disease severity (DS) from UAV data. The calculated area under disease progress curve of DS was 2,810.4 to 7,058.8\%.days for human visual scoring and 1,400.5 to 4,343.2\%.days for UAV-based scoring. Moreover, a sharper differentiation of varieties compared with visual scoring was observed in area-related parameters such as area of complete foliage (AF), area of healthy foliage (AH), and mean area of lesion by unit of foliage (Ac¯/F). These advantages provide the option to replace the laborious work of visual disease assessments in the field with a more precise, nondestructive assessment via multispectral data acquired by UAV flights. Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

@article{doi:10.1094/PDIS-12-21-2734-RE,
author = {Barreto, Abel and Ispizua Yamati, Facundo Ram\'{o}n and Varrelmann, Mark and Paulus, Stefan and Mahlein, Anne-Katrin},
title = {Disease Incidence and Severity of Cercospora Leaf Spot in Sugar Beet Assessed by Multispectral Unmanned Aerial Images and Machine Learning},
journal = {Plant Disease},
volume = {107},
number = {1},
pages = {188-200},
year = {2023},
doi = {10.1094/PDIS-12-21-2734-RE},
note ={PMID: 35581914},
URL = {https://doi.org/10.1094/PDIS-12-21-2734-RE},
eprint = {https://doi.org/10.1094/PDIS-12-21-2734-RE},
abstract = {Disease incidence (DI) and metrics of disease severity are relevant parameters for decision making in plant protection and plant breeding. To develop automated and sensor-based routines, a sugar beet variety trial was inoculated with Cercospora beticola and monitored with a multispectral camera system mounted to an unmanned aerial vehicle (UAV) over the vegetation period. A pipeline based on machine learning methods was established for image data analysis and extraction of disease-relevant parameters. Features based on the digital surface model, vegetation indices, shadow condition, and image resolution improved classification performance in comparison with using single multispectral channels in 12 and 6\% of diseased and soil regions, respectively. With a postprocessing step, area-related parameters were computed after classification. Results of this pipeline also included extraction of DI and disease severity (DS) from UAV data. The calculated area under disease progress curve of DS was 2,810.4 to 7,058.8\%.days for human visual scoring and 1,400.5 to 4,343.2\%.days for UAV-based scoring. Moreover, a sharper differentiation of varieties compared with visual scoring was observed in area-related parameters such as area of complete foliage (AF), area of healthy foliage (AH), and mean area of lesion by unit of foliage (Ac¯/F). These advantages provide the option to replace the laborious work of visual disease assessments in the field with a more precise, nondestructive assessment via multispectral data acquired by UAV flights. Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.}
}

L. Lärm, F. M. Bauer, N. Hermes, J. van der Kruk, H. Vereecken, J. Vanderborght, T. H. Nguyen, G. Lopez, S. J. Seidel, F. Ewert, A. Schnepf, and A. Klotzsche, "Multi-year belowground data of minirhizotron facilities in Selhausen," Scientific Data, 2023. doi:10.1038/s41597-023-02570-9
[BibTeX] [PDF]

The production of crops secure the human food supply, but climate change is bringing new challenges. Dynamic plant growth and corresponding environmental data are required to uncover phenotypic crop responses to the changing environment. There are many datasets on above-ground organs of crops, but roots and the surrounding soil are rarely the subject of longer term studies. Here, we present what we believe to be the first comprehensive collection of root and soil data, obtained at two minirhizotron facilities located close together that have the same local climate but differ in soil type. Both facilities have 7m-long horizontal tubes at several depths that were used for crosshole ground-penetrating radar and minirhizotron camera systems. Soil sensors provide observations at a high temporal and spatial resolution. The ongoing measurements cover five years of maize and wheat trials, including drought stress treatments and crop mixtures. We make the processed data available for use in investigating the processes within the soil–plant continuum and the root images to develop and compare image analysis methods.

@article{Lärm2023,
title={Multi-year belowground data of minirhizotron facilities in Selhausen},
author={Lärm, Lena and Bauer, Felix Maximilian and Hermes, Normen and van der Kruk, Jan and Vereecken, Harry and Vanderborght, Jan and Nguyen, Thuy Huu and Lopez, Gina and Seidel, Sabine J. and Ewert, Frank and Schnepf, Andrea and Klotzsche, Anja},
journal={Scientific Data},
year={2023},
url={https://doi.org/10.1038/s41597-023-02570-9},
doi={10.1038/s41597-023-02570-9},
abstract= {The production of crops secure the human food supply, but climate change is bringing new challenges. Dynamic plant growth and corresponding environmental data are required to uncover phenotypic crop responses to the changing environment. There are many datasets on above-ground organs of crops, but roots and the surrounding soil are rarely the subject of longer term studies. Here, we present what we believe to be the first comprehensive collection of root and soil data, obtained at two minirhizotron facilities located close together that have the same local climate but differ in soil type. Both facilities have 7m-long horizontal tubes at several depths that were used for crosshole ground-penetrating radar and minirhizotron camera systems. Soil sensors provide observations at a high temporal and spatial resolution. The ongoing measurements cover five years of maize and wheat trials, including drought stress treatments and crop mixtures. We make the processed data available for use in investigating the processes within the soil–plant continuum and the root images to develop and compare image analysis methods.}
}

T. Selzner, J. Horn, M. Landl, A. Pohlmeier, D. Helmrich, K. Huber, J. Vanderborght, H. Vereecken, S. Behnke, and A. Schnepf, "3D U-Net Segmentation Improves Root System Reconstruction from 3D MRI Images in Automated and Manual Virtual Reality Work Flows," Plant Phenomics, vol. 5, p. 76, 2023. doi:10.34133/plantphenomics.0076
[BibTeX] [PDF]

@article{doi:10.34133/plantphenomics.0076,
author = {Tobias Selzner and Jannis Horn and Magdalena Landl and Andreas Pohlmeier and Dirk Helmrich and Katrin Huber and Jan Vanderborght and Harry Vereecken and Sven Behnke and Andrea Schnepf},
title = {3D U-Net Segmentation Improves Root System Reconstruction from 3D MRI Images in Automated and Manual Virtual Reality Work Flows},
journal = {Plant Phenomics},
volume = {5},
pages = {0076},
year = {2023},
doi = {10.34133/plantphenomics.0076},
url = {https://spj.science.org/doi/abs/10.34133/plantphenomics.0076},
}

A. Bonerath, Y. Dong, and J. -H. Haunert, "An Efficient Data Structure Providing Maps of the Frequency of Public Transit Service Within User-Specified Time Windows," Advances in Cartography and GIScience of the ICA, vol. 4, p. 1, 2023. doi:10.5194/ica-adv-4-1-2023
[BibTeX] [PDF]

@Article{ica-adv-4-1-2023,
AUTHOR = {Bonerath, A. and Dong, Y. and Haunert, J.-H.},
TITLE = {An Efficient Data Structure Providing Maps of the Frequency of Public Transit Service Within User-Specified Time Windows},
JOURNAL = {Advances in Cartography and GIScience of the ICA},
VOLUME = {4},
YEAR = {2023},
PAGES = {1},
URL = {https://ica-adv.copernicus.org/articles/4/1/2023/ica-adv-4-1-2023.pdf},
DOI = {10.5194/ica-adv-4-1-2023}
}

M. I. Gocke, J. Guigue, S. L. Bauke, D. Barkusky, M. Baumecker, A. E. Berns, E. Hobley, B. Honermeier, I. Kögel-Knabner, S. Koszinski, A. Sandhage-Hofmann, U. Schmidhalter, F. Schneider, K. Schweitzer, S. Seidel, S. Siebert, L. E. Skadell, M. Sommer, S. von Tucher, A. Don, and W. Amelung, "Interactive effects of agricultural management on soil organic carbon accrual: A synthesis of long-term field experiments in Germany," Geoderma, vol. 438, p. 116616, 2023. doi:10.1016/j.geoderma.2023.116616
[BibTeX] [PDF]

Crop production often leads to soil organic carbon (SOC) losses. However, under good management practice it is possible to maintain and even re-accumulate SOC. We evaluated how different cropland management techniques affected SOC stocks in the topsoil (0–30 cm depth) of 10 long-term experiments (LTE) in Germany. We found that SOC stocks were particularly enhanced by mineral fertilization and organic amendments like straw incorporation and to a smaller degree by irrigation, but only slightly affected by the choice of preceding crops. In agreement with global meta-analyses, liming and reduced tillage had little or even negative effects on SOC storage, but effects also depended on fertilization. Management effects on SOC stocks were dependent on soil texture: sandy soils showed the lowest SOC stocks of 20.9 ± 2.3 (standard error of the mean) Mg ha−1, but exhibited the largest relative response to different management options. Annual changes in SOC stocks ranged from −3.0 ‰ with no mineral N fertilization, to + 6.1 ‰ with farmyard manure application, using the mineral-fertilized and limed treatment as reference. Even higher rates of up to + 10.6 ‰ yr−1 were reached with the combination of irrigation and straw incorporation. Note that the contribution of organic amendments to SOC accrual and thus to climate change mitigation must be adjusted for reduction in SOC at sites from which straw was removed. Overall, the potential of agricultural management to influence and enhance SOC stocks is significant. This potential is controlled by soil type and land-use duration, is largest for sandy soils with overall lowest SOC stocks, and is characterized by antagonistic and synergistic effects of different management practices.

@article{GOCKE2023116616,
title = {Interactive effects of agricultural management on soil organic carbon accrual: A synthesis of long-term field experiments in Germany},
journal = {Geoderma},
volume = {438},
pages = {116616},
year = {2023},
issn = {0016-7061},
doi = {10.1016/j.geoderma.2023.116616},
url = {https://www.sciencedirect.com/science/article/pii/S0016706123002938},
author = {Martina I. Gocke and Julien Guigue and Sara L. Bauke and Dietmar Barkusky and Michael Baumecker and Anne E. Berns and Eleanor Hobley and Bernd Honermeier and Ingrid Kögel-Knabner and Sylvia Koszinski and Alexandra Sandhage-Hofmann and Urs Schmidhalter and Florian Schneider and Kathlin Schweitzer and Sabine Seidel and Stefan Siebert and Laura E. Skadell and Michael Sommer and Sabine {von Tucher} and Axel Don and Wulf Amelung},
keywords = {Carbon stocks, Fertilization, Arable topsoil, Agricultural soil management, Soil health, Nutrients},
abstract = {Crop production often leads to soil organic carbon (SOC) losses. However, under good management practice it is possible to maintain and even re-accumulate SOC. We evaluated how different cropland management techniques affected SOC stocks in the topsoil (0–30 cm depth) of 10 long-term experiments (LTE) in Germany. We found that SOC stocks were particularly enhanced by mineral fertilization and organic amendments like straw incorporation and to a smaller degree by irrigation, but only slightly affected by the choice of preceding crops. In agreement with global meta-analyses, liming and reduced tillage had little or even negative effects on SOC storage, but effects also depended on fertilization. Management effects on SOC stocks were dependent on soil texture: sandy soils showed the lowest SOC stocks of 20.9 ± 2.3 (standard error of the mean) Mg ha−1, but exhibited the largest relative response to different management options. Annual changes in SOC stocks ranged from −3.0 ‰ with no mineral N fertilization, to + 6.1 ‰ with farmyard manure application, using the mineral-fertilized and limed treatment as reference. Even higher rates of up to + 10.6 ‰ yr−1 were reached with the combination of irrigation and straw incorporation. Note that the contribution of organic amendments to SOC accrual and thus to climate change mitigation must be adjusted for reduction in SOC at sites from which straw was removed. Overall, the potential of agricultural management to influence and enhance SOC stocks is significant. This potential is controlled by soil type and land-use duration, is largest for sandy soils with overall lowest SOC stocks, and is characterized by antagonistic and synergistic effects of different management practices.}
}

D. Wallach, T. Palosuo, P. Thorburn, H. Mielenz, S. Buis, Z. Hochman, E. Gourdain, F. Andrianasolo, B. Dumont, R. Ferrise, T. Gaiser, C. Garcia, S. Gayler, M. Harrison, S. Hiremath, H. Horan, G. Hoogenboom, P. Jansson, Q. Jing, E. Justes, K. Kersebaum, M. Launay, E. Lewan, K. Liu, F. Mequanint, M. Moriondo, C. Nendel, G. Padovan, B. Qian, N. Schütze, D. Seserman, V. Shelia, A. Souissi, X. Specka, A. K. Srivastava, G. Trombi, T. K. D. Weber, L. Weihermüller, T. Wöhling, and S. J. Seidel, "Proposal and extensive test of a calibration protocol for crop phenology models," Agronomy for Sustainable Development, vol. 43, iss. 4, 2023. doi:10.1007/s13593-023-00900-0
[BibTeX] [PDF]

A major effect of environment on crops is through crop phenology, and therefore, the capacity to predict phenology for new environments is important. Mechanistic crop models are a major tool for such predictions, but calibration of crop phenology models is difficult and there is no consensus on the best approach. We propose an original, detailed approach for calibration of such models, which we refer to as a calibration protocol. The protocol covers all the steps in the calibration workflow, namely choice of default parameter values, choice of objective function, choice of parameters to estimate from the data, calculation of optimal parameter values, and diagnostics. The major innovation is in the choice of which parameters to estimate from the data, which combines expert knowledge and data-based model selection. First, almost additive parameters are identified and estimated. This should make bias (average difference between observed and simulated values) nearly zero. These are “obligatory” parameters, that will definitely be estimated. Then candidate parameters are identified, which are parameters likely to explain the remaining discrepancies between simulated and observed values. A candidate is only added to the list of parameters to estimate if it leads to a reduction in BIC (Bayesian Information Criterion), which is a model selection criterion. A second original aspect of the protocol is the specification of documentation for each stage of the protocol. The protocol was applied by 19 modeling teams to three data sets for wheat phenology. All teams first calibrated their model using their “usual” calibration approach, so it was possible to compare usual and protocol calibration. Evaluation of prediction error was based on data from sites and years not represented in the training data. Compared to usual calibration, calibration following the new protocol reduced the variability between modeling teams by 22% and reduced prediction error by 11%.

@article{wallachcalibrationprotocol,
author={Wallach, Daniel and Palosuo, Taru and Thorburn, Peter and Mielenz, Henrike and Buis, Samuel and Hochman, Zvi and Gourdain, Emmanuelle and Andrianasolo, Fety and Dumont, Benjamin and Ferrise, Roberto and Gaiser, Thomas and Garcia, Cecile and Gayler, Sebastian and Harrison, Matthew and Hiremath, Santosh and Horan, Heidi and Hoogenboom, Gerrit and Jansson, Per-Erik and Jing, Qi and Justes, Eric and Kersebaum, Kurt-Christian and Launay, Marie and Lewan, Elisabet and Liu, Ke and Mequanint, Fasil and Moriondo, Marco and Nendel, Claas and Padovan, Gloria and Qian, Budong and Schütze, Niels and Seserman, Diana-Maria and Shelia, Vakhtang and Souissi, Amir and Specka, Xenia and Srivastava, Amit Kumar and Trombi, Giacomo and Weber, Tobias K. D. and Weihermüller, Lutz and Wöhling, Thomas and Seidel, Sabine J.},
year={2023},
journal={Agronomy for Sustainable Development},
url={https://doi.org/10.1007/s13593-023-00900-0},
doi={10.1007/s13593-023-00900-0},
volume={43},
title={Proposal and extensive test of a calibration protocol for crop phenology models},
number={4},
abstract={A major effect of environment on crops is through crop phenology, and therefore, the capacity to predict phenology for new environments is important. Mechanistic crop models are a major tool for such predictions, but calibration of crop phenology models is difficult and there is no consensus on the best approach. We propose an original, detailed approach for calibration of such models, which we refer to as a calibration protocol. The protocol covers all the steps in the calibration workflow, namely choice of default parameter values, choice of objective function, choice of parameters to estimate from the data, calculation of optimal parameter values, and diagnostics. The major innovation is in the choice of which parameters to estimate from the data, which combines expert knowledge and data-based model selection. First, almost additive parameters are identified and estimated. This should make bias (average difference between observed and simulated values) nearly zero. These are “obligatory” parameters, that will definitely be estimated. Then candidate parameters are identified, which are parameters likely to explain the remaining discrepancies between simulated and observed values. A candidate is only added to the list of parameters to estimate if it leads to a reduction in BIC (Bayesian Information Criterion), which is a model selection criterion. A second original aspect of the protocol is the specification of documentation for each stage of the protocol. The protocol was applied by 19 modeling teams to three data sets for wheat phenology. All teams first calibrated their model using their “usual” calibration approach, so it was possible to compare usual and protocol calibration. Evaluation of prediction error was based on data from sites and years not represented in the training data. Compared to usual calibration, calibration following the new protocol reduced the variability between modeling teams by 22% and reduced prediction error by 11%.}}

M. Giraud, S. L. Gall, M. Harings, M. Javaux, D. Leitner, F. Meunier, Y. Rothfuss, D. van Dusschoten, J. Vanderborght, H. Vereecken, G. Lobet, and A. Schnepf, "CPlantBox: a fully coupled modelling platform for the water and carbon fluxes in the soil–plant–atmosphere continuum," in silico Plants, vol. 5, iss. 2, p. diad009, 2023. doi:10.1093/insilicoplants/diad009
[BibTeX] [PDF] [Video]

{A plant’s development is strongly linked to the water and carbon flows in the soil–plant–atmosphere continuum. Expected climate shifts will alter the water and carbon cycles and will affect plant phenotypes. Comprehensive models that simulate mechanistically and dynamically the feedback loops between a plant’s three-dimensional development and the water and carbon flows are useful tools to evaluate the sustainability of genotype–environment–management combinations which do not yet exist. In this study, we present the latest version of the open-source three-dimensional Functional–Structural Plant Model CPlantBox with PiafMunch and DuMu\\$\\{\\}^\\{\\text\\{x\\}\\}\\$ coupling. This new implementation can be used to study the interactions between known or hypothetical processes at the plant scale. We simulated semi-mechanistically the development of generic C3 monocots from 10 to 25 days after sowing and undergoing an atmospheric dry spell of 1 week (no precipitation). We compared the results for dry spells starting on different days (Day 11 or 18) against a wetter and colder baseline scenario. Compared with the baseline, the dry spells led to a lower instantaneous water-use efficiency. Moreover, the temperature-induced increased enzymatic activity led to a higher maintenance respiration which diminished the amount of sucrose available for growth. Both of these effects were stronger for the later dry spell compared with the early dry spell. We could thus use CPlantBox to simulate diverging emerging processes (like carbon partitioning) defining the plants’ phenotypic plasticity response to their environment. The model remains to be validated against independent observations of the soil–plant–atmosphere continuum.}

@article{10.1093/insilicoplants/diad009,
author = {Giraud, Mona and Gall, Samuel Le and Harings, Moritz and Javaux, Mathieu and Leitner, Daniel and Meunier, Félicien and Rothfuss, Youri and van Dusschoten, Dagmar and Vanderborght, Jan and Vereecken, Harry and Lobet, Guillaume and Schnepf, Andrea},
title = "{CPlantBox: a fully coupled modelling platform for the water and carbon fluxes in the soil–plant–atmosphere continuum}",
journal = {in silico Plants},
volume = {5},
number = {2},
pages = {diad009},
year = {2023},
month = {07},
abstract = "{A plant’s development is strongly linked to the water and carbon flows in the soil–plant–atmosphere continuum. Expected climate shifts will alter the water and carbon cycles and will affect plant phenotypes. Comprehensive models that simulate mechanistically and dynamically the feedback loops between a plant’s three-dimensional development and the water and carbon flows are useful tools to evaluate the sustainability of genotype–environment–management combinations which do not yet exist. In this study, we present the latest version of the open-source three-dimensional Functional–Structural Plant Model CPlantBox with PiafMunch and DuMu\\$\\{\\}^\\{\\text\\{x\\}\\}\\$ coupling. This new implementation can be used to study the interactions between known or hypothetical processes at the plant scale. We simulated semi-mechanistically the development of generic C3 monocots from 10 to 25 days after sowing and undergoing an atmospheric dry spell of 1 week (no precipitation). We compared the results for dry spells starting on different days (Day 11 or 18) against a wetter and colder baseline scenario. Compared with the baseline, the dry spells led to a lower instantaneous water-use efficiency. Moreover, the temperature-induced increased enzymatic activity led to a higher maintenance respiration which diminished the amount of sucrose available for growth. Both of these effects were stronger for the later dry spell compared with the early dry spell. We could thus use CPlantBox to simulate diverging emerging processes (like carbon partitioning) defining the plants’ phenotypic plasticity response to their environment. The model remains to be validated against independent observations of the soil–plant–atmosphere continuum.}",
issn = {2517-5025},
doi = {10.1093/insilicoplants/diad009},
videourl = {https://www.youtube.com/watch?v=-YgDnsC3BV8},
url = {https://doi.org/10.1093/insilicoplants/diad009},
eprint = {https://academic.oup.com/insilicoplants/article-pdf/5/2/diad009/51684855/diad009.pdf},
}

J. Yi, G. Lopez, S. Hadir, J. Weyler, L. Klingbeil, M. Deichmann, J. Gall, and S. J. Seidel, "Non-Invasive Diagnosis of Nutrient Deficiencies in Winter Wheat and Winter Rye Using Uav-Based Rgb Images," Available at SSRN 4549653, 2023. doi:10.2139/ssrn.4549653
[BibTeX] [PDF]

@article{yi4549653non,
title={Non-Invasive Diagnosis of Nutrient Deficiencies in Winter Wheat and Winter Rye Using Uav-Based Rgb Images},
author={Yi, Jinhui and Lopez, Gina and Hadir, Sofia and Weyler, Jan and Klingbeil, Lasse and Deichmann, Marion and Gall, Juergen and Seidel, Sabine J},
journal={Available at SSRN 4549653},
year={2023},
doi={10.2139/ssrn.4549653},
url={https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4549653},
}

R. A. Rosu and S. Behnke, "PermutoSDF: Fast Multi-View Reconstruction with Implicit Surfaces Using Permutohedral Lattices," in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2023, pp. 8466-8475. doi:10.1109/CVPR52729.2023.00818
[BibTeX] [PDF] [Code] [Video]

@INPROCEEDINGS{10203691,
author={Rosu, Radu Alexandru and Behnke, Sven},
booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
title={PermutoSDF: Fast Multi-View Reconstruction with Implicit Surfaces Using Permutohedral Lattices},
year={2023},
volume={},
number={},
url={https://www.ais.uni-bonn.de/papers/CVPR_2023_Rosu.pdf},
codeurl={https://github.com/RaduAlexandru/permuto_sdf},
videourl={https://www.youtube.com/watch?v=ElpdDxJHLVE}, pages={8466-8475},
doi={10.1109/CVPR52729.2023.00818}}

F. Esser, R. A. Rosu, A. Cornelißen, L. Klingbeil, H. Kuhlmann, and S. Behnke, "Field Robot for High-Throughput and High-Resolution 3D Plant Phenotyping: Towards Efficient and Sustainable Crop Production," IEEE Robotics & Automation Magazine, pp. 2-11, 2023. doi:10.1109/MRA.2023.3321402
[BibTeX] [PDF]

@ARTICLE{10302421,
author={Esser, Felix and Rosu, Radu Alexandru and Cornelißen, André and Klingbeil, Lasse and Kuhlmann, Heiner and Behnke, Sven},
journal={IEEE Robotics & Automation Magazine},
title={Field Robot for High-Throughput and High-Resolution 3D Plant Phenotyping: Towards Efficient and Sustainable Crop Production},
year={2023},
volume={},
number={},
pages={2-11},
doi={10.1109/MRA.2023.3321402},
URL = {https://arxiv.org/pdf/2310.11516},}

T. Zaenker, J. Rückin, M. Popovic, and M. Bennewitz, "Graph-based View Motion Planning for Fruit Detection," in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2023.
[BibTeX] [PDF] [Code] [Video]

@INPROCEEDINGS{zaenkerirosfruitdetection,
author={Zaenker, Tobias and Rückin, Julius and Popovic, Marija and Bennewitz, Maren},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
title={Graph-based View Motion Planning for Fruit Detection},
year={2023},
codeurl={https://github.com/Eruvae/view_motion_planner},
videourl={https://www.youtube.com/watch?v=Svko7DZXStk},
url={https://arxiv.org/pdf/2303.03048}}

C. Liu, A. Mentzelopoulou, F. Papagavriil, P. Ramachandran, A. Perraki, L. Claus, S. Barg, P. Dörmann, Y. Jaillais, P. Johnen, E. Russinova, E. Gizeli, G. Schaaf, and P. N. Moschou, "SEC14-like condensate phase transitions at plasma membranes regulate root growth in Arabidopsis," PLoS biology, 2023. doi:10.1371/journal.pbio.3002305
[BibTeX] [PDF]

Protein function can be modulated by phase transitions in their material properties, which can range from liquid- to solid-like; yet, the mechanisms that drive these transitions and whether they are important for physiology are still unknown. In the model plant Arabidopsis, we show that developmental robustness is reinforced by phase transitions of the plasma membrane-bound lipid-binding protein SEC14-like. Using imaging, genetics, and in vitro reconstitution experiments, we show that SEC14-like undergoes liquid-like phase separation in the root stem cells. Outside the stem cell niche, SEC14-like associates with the caspase-like protease separase and conserved microtubule motors at unique polar plasma membrane interfaces. In these interfaces, SEC14-like undergoes processing by separase, which promotes its liquid-to-solid transition. This transition is important for root development, as lines expressing an uncleavable SEC14-like variant or mutants of separase and associated microtubule motors show similar developmental phenotypes. Furthermore, the processed and solidified but not the liquid form of SEC14-like interacts with and regulates the polarity of the auxin efflux carrier PINFORMED2. This work demonstrates that robust development can involve liquid-to-solid transitions mediated by proteolysis at unique plasma membrane interfaces.

@article{liuplos,
title={SEC14-like condensate phase transitions at plasma membranes regulate root growth
in Arabidopsis},
abstract={Protein function can be modulated by phase transitions in their material
properties, which can range from liquid- to solid-like; yet, the mechanisms that
drive these transitions and whether they are important for physiology are still
unknown. In the model plant Arabidopsis, we show that developmental robustness is
reinforced by phase transitions of the plasma membrane-bound lipid-binding
protein SEC14-like. Using imaging, genetics, and in vitro reconstitution
experiments, we show that SEC14-like undergoes liquid-like phase separation in
the root stem cells. Outside the stem cell niche, SEC14-like associates with the
caspase-like protease separase and conserved microtubule motors at unique polar
plasma membrane interfaces. In these interfaces, SEC14-like undergoes processing
by separase, which promotes its liquid-to-solid transition. This transition is
important for root development, as lines expressing an uncleavable SEC14-like
variant or mutants of separase and associated microtubule motors show similar
developmental phenotypes. Furthermore, the processed and solidified but not the
liquid form of SEC14-like interacts with and regulates the polarity of the auxin
efflux carrier PINFORMED2. This work demonstrates that robust development can
involve liquid-to-solid transitions mediated by proteolysis at unique plasma
membrane interfaces.},
author={Liu, Chen and Mentzelopoulou, Andriani and Papagavriil, Fotini and Ramachandran, Prashanth and Perraki, Artemis and Claus, Lucas and Barg, Sebastian and Dörmann, Peter and Jaillais, Yvon and Johnen, Philipp and Russinova, Eugenia and Gizeli, Electra and Schaaf, Gabriel and Moschou, Panagiotis Nikolaou},
journal={PLoS biology},
doi={10.1371/journal.pbio.3002305},
url={https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10538751/},
year={2023}}

R. Menon, T. Zaenker, N. Dengler, and M. Bennewitz, "NBV-SC: Next Best View Planning based on Shape Completion for Fruit Mapping and Reconstruction," in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2023. doi:10.48550/arXiv.2209.15376
[BibTeX] [PDF] [Video]

@InProceedings{menon2023nbvsc,
title={NBV-SC: Next Best View Planning based on Shape Completion for Fruit Mapping and Reconstruction},
author={Rohit Menon and Tobias Zaenker and Nils Dengler and Maren Bennewitz},
year={2023},
doi={10.48550/arXiv.2209.15376},
url={https://arxiv.org/pdf/2209.15376},
videourl={https://www.youtube.com/watch?v=AmrNkyCDZo8},
eprint={2209.15376},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}
}

M. Li, M. Halstead, and C. McCool, "Knowledge Distillation for Efficient Panoptic Semantic Segmentation: Applied to Agriculture," in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2023. doi:10.1109/IROS55552.2023.10342527
[BibTeX]

@InProceedings{Li23_panopticknowledgedistillation,
author = {M. Li and M. Halstead and C. McCool},
title = {Knowledge Distillation for Efficient Panoptic Semantic
Segmentation: Applied to Agriculture},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year = {2023},
doi={10.1109/IROS55552.2023.10342527}
}

I. M. Hernández-Ochoa, T. Gaiser, H. Hüging, and F. Ewert, "Yield components and yield quality of old and modern wheat cultivars as affected by cultivar release date, N fertilization and environment in Germany," Field Crops Research, vol. 302, p. 109094, 2023. doi:10.1016/j.fcr.2023.109094
[BibTeX] [PDF]

Wheat (Triticum aestivum L.) is the main staple crop worldwide. Understanding the genotype × management × environment (G × M × E) interaction is critical for breeding, yield, grain quality estimation and to develop sustainable production systems that meet food and feed demand. Many studies have explored grain yield changes and yield stability over time, as well as G × M × E (i.e., the effect of breeding progress), but limited studies have looked at these effects on yield components and grain quality parameters. A field experiment was conducted to understand the G × M × E interactions for yield components of winter wheat cultivars. The study was carried out for three seasons (2015–2018) using a split plot design with four replications, with nitrogen (N) fertilizer levels (0, 120 and 240 kg N ha-1), in the main plot and cultivars in the subplot. Three old cultivars (released before 1960), Heines II, Heines Rot, Heines VII and three modern cultivars (released after 1960), Jubilar, Sperber and Tommi, were selected. Total aboveground biomass (TAGB), grain yield, harvest index (HI), thousand kernel weight (TKW), grains m-2, grain protein and hectoliter weight (HLW) were collected at harvest. The three-way interaction was significant for most variables except for grain protein, but after applying the AIC method, the three-way interaction was just relevant for HI. The 0 N level always resulted in lowest TAGB, grain yield, grains m-2, grain protein and HLW for all cultivars. All cultivars showed a positive response in grain yield when applying 120 kg ha-1 of N but no significant further yield increase was observed with higher N levels. With regards to HI, cultivar differences were more apparent during the wettest year 2016. The HI tends to decrease with increasing N levels and was the highest in modern cultivars with Tommi and Sperber, though in 2017, also Heines VII showed one of the highest HI across treatments. Grain protein content progressively increased with N level, but newer cultivars like Tommi and Sperber, showed some of the lowest protein contents. Yield component contribution to grain yield has changed over time with grain number, grain size and HI, becoming more important for yield realization. Modern cultivars, especially Tommi, performed better than the rest of the cultivars under sufficient N supply. Optimal N-rates among cultivars differ for grain yield and grain protein, posing a challenge to maintain wheat supply to the increasing wheat demand in the future but also to meeting sustainability targets to reduce and optimize resource use.

@article{HERNANDEZOCHOA2023109094,
title = {Yield components and yield quality of old and modern wheat cultivars as affected by cultivar release date, N fertilization and environment in Germany},
journal = {Field Crops Research},
volume = {302},
pages = {109094},
year = {2023},
issn = {0378-4290},
doi = {10.1016/j.fcr.2023.109094},
url = {https://www.sciencedirect.com/science/article/pii/S0378429023002873},
author = {Ixchel M. Hernández-Ochoa and Thomas Gaiser and Hubert Hüging and Frank Ewert},
keywords = {Breeding progress, Winter wheat, Nitrogen supply, Cereals, Crop nutrients},
abstract = {Wheat (Triticum aestivum L.) is the main staple crop worldwide. Understanding the genotype × management × environment (G × M × E) interaction is critical for breeding, yield, grain quality estimation and to develop sustainable production systems that meet food and feed demand. Many studies have explored grain yield changes and yield stability over time, as well as G × M × E (i.e., the effect of breeding progress), but limited studies have looked at these effects on yield components and grain quality parameters. A field experiment was conducted to understand the G × M × E interactions for yield components of winter wheat cultivars. The study was carried out for three seasons (2015–2018) using a split plot design with four replications, with nitrogen (N) fertilizer levels (0, 120 and 240 kg N ha-1), in the main plot and cultivars in the subplot. Three old cultivars (released before 1960), Heines II, Heines Rot, Heines VII and three modern cultivars (released after 1960), Jubilar, Sperber and Tommi, were selected. Total aboveground biomass (TAGB), grain yield, harvest index (HI), thousand kernel weight (TKW), grains m-2, grain protein and hectoliter weight (HLW) were collected at harvest. The three-way interaction was significant for most variables except for grain protein, but after applying the AIC method, the three-way interaction was just relevant for HI. The 0 N level always resulted in lowest TAGB, grain yield, grains m-2, grain protein and HLW for all cultivars. All cultivars showed a positive response in grain yield when applying 120 kg ha-1 of N but no significant further yield increase was observed with higher N levels. With regards to HI, cultivar differences were more apparent during the wettest year 2016. The HI tends to decrease with increasing N levels and was the highest in modern cultivars with Tommi and Sperber, though in 2017, also Heines VII showed one of the highest HI across treatments. Grain protein content progressively increased with N level, but newer cultivars like Tommi and Sperber, showed some of the lowest protein contents. Yield component contribution to grain yield has changed over time with grain number, grain size and HI, becoming more important for yield realization. Modern cultivars, especially Tommi, performed better than the rest of the cultivars under sufficient N supply. Optimal N-rates among cultivars differ for grain yield and grain protein, posing a challenge to maintain wheat supply to the increasing wheat demand in the future but also to meeting sustainability targets to reduce and optimize resource use.}
}

L. T. Dinh, Y. Ueda, D. Gonzalez, J. P. Tanaka, H. Takanashi, and M. Wissuwa, "Novel QTL for Lateral Root Density and Length Improve Phosphorus Uptake in Rice (Oryza sativa L.)," Rice, vol. 16, 2023. doi:10.1186/s12284-023-00654-z
[BibTeX] [PDF]

The rice root system consists of two types of lateral roots, indeterminate larger L-types capable of further branching, and determinate, short, unbranched S-types. L-type laterals correspond to the typical lateral roots of cereals whereas S-type laterals are unique to rice. Both types contribute to nutrient and water uptake and genotypic variation for density and length of these laterals could be exploited in rice improvement to enhance adaptations to nutrient and water-limited environments. Our objectives were to determine how best to screen for lateral root density and length and to identify markers linked to genotypic variation for these traits. Using different growing media showed that screening in nutrient solution exposed genotypic variation for S-type and L-type density, but only the lateral roots of soil-grown plants varied for their lengths. A QTL mapping population developed from parents contrasting for lateral root traits was grown in a low-P field, roots were sampled, scanned and density and length of lateral roots measured. One QTL each was detected for L-type density (LDC), S-type density on crown root (SDC), S-type density on L-type (SDL), S-type length on L-type (SLL), and crown root number (RNO). The QTL for LDC on chromosome 5 had a major effect, accounting for 46% of the phenotypic variation. This strong positive effect was confirmed in additional field experiments, showing that lines with the donor parent allele at qLDC5 had 50% higher LDC. Investigating the contribution of lateral root traits to P uptake using stepwise regressions indicated LDC and RNO were most influential, followed by SDL. Simulating effects of root trait differences conferred by the main QTL in a P uptake model confirmed that qLDC5 was most effective in improving P uptake followed by qRNO9 for RNO and qSDL9 for S-type lateral density on L-type laterals. Pyramiding qLDC5 with qRNO9 and qSDL9 would be possible given that trade-offs between traits were not detected. Phenotypic selection for the RNO trait during variety development would be feasible, however, the costs of doing so reliably for lateral root density traits is prohibitive and markers identified here therefore provide the first opportunity to incorporate such traits into a breeding program.

@article{dinhrice,
author = {Lam Thi Dinh and Yoshiaki Ueda and Daniel Gonzalez and Juan Pariasca Tanaka and Hideki Takanashi and Matthias Wissuwa},
title = {Novel QTL for Lateral Root Density and Length Improve Phosphorus Uptake in Rice (Oryza sativa L.)},
journal = {Rice},
volume = {16},
year = {2023},
issue = {1},
url = {https://doi.org/10.1186/s12284-023-00654-z},
doi = {10.1186/s12284-023-00654-z},
abstract = {The rice root system consists of two types of lateral roots, indeterminate larger L-types capable of further branching, and determinate, short, unbranched S-types. L-type laterals correspond to the typical lateral roots of cereals whereas S-type laterals are unique to rice. Both types contribute to nutrient and water uptake and genotypic variation for density and length of these laterals could be exploited in rice improvement to enhance adaptations to nutrient and water-limited environments. Our objectives were to determine how best to screen for lateral root density and length and to identify markers linked to genotypic variation for these traits. Using different growing media showed that screening in nutrient solution exposed genotypic variation for S-type and L-type density, but only the lateral roots of soil-grown plants varied for their lengths. A QTL mapping population developed from parents contrasting for lateral root traits was grown in a low-P field, roots were sampled, scanned and density and length of lateral roots measured. One QTL each was detected for L-type density (LDC), S-type density on crown root (SDC), S-type density on L-type (SDL), S-type length on L-type (SLL), and crown root number (RNO). The QTL for LDC on chromosome 5 had a major effect, accounting for 46% of the phenotypic variation. This strong positive effect was confirmed in additional field experiments, showing that lines with the donor parent allele at qLDC5 had 50% higher LDC. Investigating the contribution of lateral root traits to P uptake using stepwise regressions indicated LDC and RNO were most influential, followed by SDL. Simulating effects of root trait differences conferred by the main QTL in a P uptake model confirmed that qLDC5 was most effective in improving P uptake followed by qRNO9 for RNO and qSDL9 for S-type lateral density on L-type laterals. Pyramiding qLDC5 with qRNO9 and qSDL9 would be possible given that trade-offs between traits were not detected. Phenotypic selection for the RNO trait during variety development would be feasible, however, the costs of doing so reliably for lateral root density traits is prohibitive and markers identified here therefore provide the first opportunity to incorporate such traits into a breeding program.}
}

T. A. P. West, S. Wunder, E. O. Sills, J. Börner, S. W. Rifai, A. N. Neidermeier, G. P. Frey, and A. Kontoleon, "Action needed to make carbon offsets from forest conservation work for climate change mitigation," Science, vol. 381, iss. 6660, pp. 873-877, 2023. doi:10.1126/science.ade3535
[BibTeX]

Carbon offsets from voluntary avoided-deforestation projects are generated on the basis of performance in relation to ex ante deforestation baselines. We examined the effects of 26 such project sites in six countries on three continents using synthetic control methods for causal inference. We found that most projects have not significantly reduced deforestation. For projects that did, reductions were substantially lower than claimed. This reflects differences between the project ex ante baselines and ex post counterfactuals according to observed deforestation in control areas. Methodologies used to construct deforestation baselines for carbon offset interventions need urgent revisions to correctly attribute reduced deforestation to the projects, thus maintaining both incentives for forest conservation and the integrity of global carbon accounting. Reducing emissions from deforestation and forest degradation (REDD) projects are intended to decrease carbon emissions from forests to offset other carbon emissions and are often claimed as credits to be used in calculating carbon emission budgets. West et al. compared the actual effects of these projects with measurable baseline values and found that most of them have not reduced deforestation significantly, and those that did had benefits substantially lower than claimed (see the Perspective by Jones and Lewis). Thus, most REDD projects are less beneficial than is often claimed. —H. Jesse Smith Most REDD projects deliver little to no decrease in deforestation and forest degradation.

@article{doi:10.1126/science.ade3535,
author = {Thales A. P. West and Sven Wunder and Erin O. Sills and Jan Börner and Sami W. Rifai and Alexandra N. Neidermeier and Gabriel P. Frey and Andreas Kontoleon},
title = {Action needed to make carbon offsets from forest conservation work for climate change mitigation},
journal = {Science},
volume = {381},
number = {6660},
pages = {873-877},
year = {2023},
doi = {10.1126/science.ade3535},
abstract = {Carbon offsets from voluntary avoided-deforestation projects are generated on the basis of performance in relation to ex ante deforestation baselines. We examined the effects of 26 such project sites in six countries on three continents using synthetic control methods for causal inference. We found that most projects have not significantly reduced deforestation. For projects that did, reductions were substantially lower than claimed. This reflects differences between the project ex ante baselines and ex post counterfactuals according to observed deforestation in control areas. Methodologies used to construct deforestation baselines for carbon offset interventions need urgent revisions to correctly attribute reduced deforestation to the projects, thus maintaining both incentives for forest conservation and the integrity of global carbon accounting. Reducing emissions from deforestation and forest degradation (REDD) projects are intended to decrease carbon emissions from forests to offset other carbon emissions and are often claimed as credits to be used in calculating carbon emission budgets. West et al. compared the actual effects of these projects with measurable baseline values and found that most of them have not reduced deforestation significantly, and those that did had benefits substantially lower than claimed (see the Perspective by Jones and Lewis). Thus, most REDD projects are less beneficial than is often claimed. —H. Jesse Smith Most REDD projects deliver little to no decrease in deforestation and forest degradation.}}

C. Hubert, S. Tsiaparas, L. Kahlert, K. Luhmer, M. D. Moll, M. Passon, M. Wüst, A. Schieber, and R. Pude, "Effect of Different Postharvest Methods on Essential Oil Content and Composition of Three Mentha Genotypes," Horticulturae, vol. 9, iss. 9, 2023. doi:10.3390/horticulturae9090960
[BibTeX] [PDF] [Video]

Mentha sp. is commonly used for essential oil (EO) extraction and incorporated in multiple products of food and pharmaceutical industries. Postharvest management is a key factor in line of production to preserve quality-determining plant ingredients. This study focused on the effects of two different postharvest processes on EO content and the composition of three different Mentha genotypes (Mentha × piperita ‘Multimentha’, Mentha × piperita ‘Fränkische Blaue’ and Mentha rotundifolia ‘Apfelminze’). They were cultivated under greenhouse conditions. One postharvest treatment consisted of drying Mentha as whole plant after harvesting and later separating leaves from stems. In the second treatment, leaves were separated from stems directly after harvesting and then dried. EO content was determined by steam distillation and composition of EO was characterized by GC/MS analysis. Key findings of the study are that the postharvest processing treatments had no significant influence on the content or composition of the EO. Only the genotype ‘Fränkische Blaue’ showed a significantly higher EO content in the dry separated treatment at the third harvest (2.9 ± 0.15 mL/100 g DM (sD)) than separated fresh (2.4 ± 0.24 mL/100 g DM (sF)). However, genotype selection and harvest time had a clear impact on EO content and composition.

@Article{horticulturae9090960,
AUTHOR = {Hubert, Charlotte and Tsiaparas, Saskia and Kahlert, Liane and Luhmer, Katharina and Moll, Marcel Dieter and Passon, Maike and Wüst, Matthias and Schieber, Andreas and Pude, Ralf},
TITLE = {Effect of Different Postharvest Methods on Essential Oil Content and Composition of Three Mentha Genotypes},
JOURNAL = {Horticulturae},
VOLUME = {9},
YEAR = {2023},
NUMBER = {9},
ARTICLE-NUMBER = {960},
VIDEOURL = {https://www.youtube.com/watch?v=Ons3IdlxYw0},
URL = {https://www.mdpi.com/2311-7524/9/9/960},
ISSN = {2311-7524},
ABSTRACT = {Mentha sp. is commonly used for essential oil (EO) extraction and incorporated in multiple products of food and pharmaceutical industries. Postharvest management is a key factor in line of production to preserve quality-determining plant ingredients. This study focused on the effects of two different postharvest processes on EO content and the composition of three different Mentha genotypes (Mentha × piperita ‘Multimentha’, Mentha × piperita ‘Fränkische Blaue’ and Mentha rotundifolia ‘Apfelminze’). They were cultivated under greenhouse conditions. One postharvest treatment consisted of drying Mentha as whole plant after harvesting and later separating leaves from stems. In the second treatment, leaves were separated from stems directly after harvesting and then dried. EO content was determined by steam distillation and composition of EO was characterized by GC/MS analysis. Key findings of the study are that the postharvest processing treatments had no significant influence on the content or composition of the EO. Only the genotype ‘Fränkische Blaue’ showed a significantly higher EO content in the dry separated treatment at the third harvest (2.9 ± 0.15 mL/100 g DM (sD)) than separated fresh (2.4 ± 0.24 mL/100 g DM (sF)). However, genotype selection and harvest time had a clear impact on EO content and composition.},
DOI = {10.3390/horticulturae9090960}
}

M. Tazifor Tchantcho, E. Zimmermann, J. A. Huisman, M. Dick, A. Mester, and S. van Waasen, "Low-Pass Filters for a Temperature Drift Correction Method for Electromagnetic Induction Systems," Sensors, vol. 23, iss. 17, 2023. doi:10.3390/s23177322
[BibTeX] [PDF]

Electromagnetic induction (EMI) systems are used for mapping the soil’s electrical conductivity in near-surface applications. EMI measurements are commonly affected by time-varying external environmental factors, with temperature fluctuations being a big contributing factor. This makes it challenging to obtain stable and reliable data from EMI measurements. To mitigate these temperature drift effects, it is customary to perform a temperature drift calibration of the instrument in a temperature-controlled environment. This involves recording the apparent electrical conductivity (ECa) values at specific temperatures to obtain a look-up table that can subsequently be used for static ECa drift correction. However, static drift correction does not account for the delayed thermal variations of the system components, which affects the accuracy of drift correction. Here, a drift correction approach is presented that accounts for delayed thermal variations of EMI system components using two low-pass filters (LPF). Scenarios with uniform and non-uniform temperature distributions in the measurement device are both considered. The approach is developed using a total of 15 measurements with a custom-made EMI device in a wide range of temperature conditions ranging from 10 °C to 50 °C. The EMI device is equipped with eight temperature sensors spread across the device that simultaneously measure the internal ambient temperature during measurements. To parameterize the proposed correction approach, a global optimization algorithm called Shuffled Complex Evolution (SCE-UA) was used for efficient estimation of the calibration parameters. Using the presented drift model to perform corrections for each individual measurement resulted in a root mean square error (RMSE) of <1 mSm−1 for all 15 measurements. This shows that the drift model can properly describe the drift of the measurement device. Performing a drift correction simultaneously for all datasets resulted in a RMSE <1.2 mSm−1, which is considerably lower than the RMSE values of up to 4.5 mSm−1 obtained when using only a single LPF to perform drift corrections. This shows that the presented drift correction method based on two LPFs is more appropriate and effective for mitigating temperature drift effects.

@Article{s23177322,
AUTHOR = {Tazifor Tchantcho, Martial and Zimmermann, Egon and Huisman, Johan Alexander and Dick, Markus and Mester, Achim and van Waasen, Stefan},
TITLE = {Low-Pass Filters for a Temperature Drift Correction Method for Electromagnetic Induction Systems},
JOURNAL = {Sensors},
VOLUME = {23},
YEAR = {2023},
NUMBER = {17},
ARTICLE-NUMBER = {7322},
URL = {https://www.mdpi.com/1424-8220/23/17/7322},
ISSN = {1424-8220},
ABSTRACT = {Electromagnetic induction (EMI) systems are used for mapping the soil’s electrical conductivity in near-surface applications. EMI measurements are commonly affected by time-varying external environmental factors, with temperature fluctuations being a big contributing factor. This makes it challenging to obtain stable and reliable data from EMI measurements. To mitigate these temperature drift effects, it is customary to perform a temperature drift calibration of the instrument in a temperature-controlled environment. This involves recording the apparent electrical conductivity (ECa) values at specific temperatures to obtain a look-up table that can subsequently be used for static ECa drift correction. However, static drift correction does not account for the delayed thermal variations of the system components, which affects the accuracy of drift correction. Here, a drift correction approach is presented that accounts for delayed thermal variations of EMI system components using two low-pass filters (LPF). Scenarios with uniform and non-uniform temperature distributions in the measurement device are both considered. The approach is developed using a total of 15 measurements with a custom-made EMI device in a wide range of temperature conditions ranging from 10 °C to 50 °C. The EMI device is equipped with eight temperature sensors spread across the device that simultaneously measure the internal ambient temperature during measurements. To parameterize the proposed correction approach, a global optimization algorithm called Shuffled Complex Evolution (SCE-UA) was used for efficient estimation of the calibration parameters. Using the presented drift model to perform corrections for each individual measurement resulted in a root mean square error (RMSE) of <1 mSm−1 for all 15 measurements. This shows that the drift model can properly describe the drift of the measurement device. Performing a drift correction simultaneously for all datasets resulted in a RMSE <1.2 mSm−1, which is considerably lower than the RMSE values of up to 4.5 mSm−1 obtained when using only a single LPF to perform drift corrections. This shows that the presented drift correction method based on two LPFs is more appropriate and effective for mitigating temperature drift effects.},
DOI = {10.3390/s23177322}
}

A. Pandey, L. Wu, V. Murugaiyan, G. Schaaf, J. Ali, and M. Frei, "Differential effects of arsenite and arsenate on rice (Oryza sativa) plants differing in glutathione S-transferase gene expression," Environmental Science and Pollution Research, 2023. doi:10.1007/s11356-023-28833-x
[BibTeX] [PDF]

Contamination of paddy soils with arsenic (As) can cause phytotoxicity in rice and increase the accumulation of arsenic in grains. The uptake and accumulation of As in rice depends on the different As species present in the soil. Plants detoxify As by conjugating and sequestering xenobiotic compounds into vacuoles using various enzymes. However, the severity of damage induced by arsenite (As(III)) and arsenate (As(V)), as well as the roles of glutathione S-transferase in detoxifying these As species in rice, are not fully understood. In this study, we developed plant materials overexpressing a glutathione S-transferase gene OsGSTU40 under the control of the maize UBIL promoter. Through systematic investigations of both wild-type Nipponbare (Oryza sativa L., ssp. japonica) and OsGSTU40 overexpression lines under chronic or acute stress of As, we aimed to understand the toxic effects of both As(III) and As(V) on rice plants at the vegetative growth stage. We hypothesized that (i) As(III) and As(V) have different toxic effects on rice plants and (ii) OsGSTU40 played positive roles in As toxicity tolerance. Our results showed that As(III) was more detrimental to plant growth than As(V) in terms of plant growth, biomass, and lipid peroxidation in both chronic and acute exposure. Furthermore, overexpression of OsGSTU40 led to better plant growth even though uptake of As(V), but not As(III), into shoots was enhanced in transgenic plants. In acute As(III) stress, transgenic plants exhibited a lower level of lipid peroxidation than wild-type plants. The element composition of plants was dominated by the different As stress treatments rather than by the genotype, while the As concentration was negatively correlated with phosphorus and silicon. Overall, our findings suggest that As(III) is more toxic to plants than As(V) and that glutathione S-transferase OsGSTU40 differentially affects plant reactions and tolerance to different species of arsenic.

@article{arsenite_aresnate,
author = {Pandey, Ambika and Wu, Lin-Bo and Murugaiyan, Varunseelan and Schaaf, Gabriel and Ali, Jauhar and Frei, Michael},
title = {Differential effects of arsenite and arsenate on rice (Oryza sativa) plants differing in glutathione S-transferase gene expression},
journal = {Environmental Science and Pollution Research},
year = {2023},
url = {https://doi.org/10.1007/s11356-023-28833-x},
doi = {10.1007/s11356-023-28833-x},
abstract = {Contamination of paddy soils with arsenic (As) can cause phytotoxicity in rice and increase the accumulation of arsenic in grains. The uptake and accumulation of As in rice depends on the different As species present in the soil. Plants detoxify As by conjugating and sequestering xenobiotic compounds into vacuoles using various enzymes. However, the severity of damage induced by arsenite (As(III)) and arsenate (As(V)), as well as the roles of glutathione S-transferase in detoxifying these As species in rice, are not fully understood. In this study, we developed plant materials overexpressing a glutathione S-transferase gene OsGSTU40 under the control of the maize UBIL promoter. Through systematic investigations of both wild-type Nipponbare (Oryza sativa L., ssp. japonica) and OsGSTU40 overexpression lines under chronic or acute stress of As, we aimed to understand the toxic effects of both As(III) and As(V) on rice plants at the vegetative growth stage. We hypothesized that (i) As(III) and As(V) have different toxic effects on rice plants and (ii) OsGSTU40 played positive roles in As toxicity tolerance. Our results showed that As(III) was more detrimental to plant growth than As(V) in terms of plant growth, biomass, and lipid peroxidation in both chronic and acute exposure. Furthermore, overexpression of OsGSTU40 led to better plant growth even though uptake of As(V), but not As(III), into shoots was enhanced in transgenic plants. In acute As(III) stress, transgenic plants exhibited a lower level of lipid peroxidation than wild-type plants. The element composition of plants was dominated by the different As stress treatments rather than by the genotype, while the As concentration was negatively correlated with phosphorus and silicon. Overall, our findings suggest that As(III) is more toxic to plants than As(V) and that glutathione S-transferase OsGSTU40 differentially affects plant reactions and tolerance to different species of arsenic.}
}

T. Rajonandraina, Y. Ueda, M. Wissuwa, G. J. D. Kirk, T. Rakotoson, H. Manwaring, A. Andriamananjara, and T. Razafimbelo, "Magnesium supply alleviates iron toxicity-induced leaf bronzing in rice through exclusion and tissue-tolerance mechanisms," Frontiers in Plant Science, vol. 14, 2023. doi:10.3389/fpls.2023.1213456
[BibTeX] [PDF]

IntroductionIron (Fe) toxicity is a widespread nutritional disorder in lowland rice causing growth retardation and leaf symptoms referred to as leaf bronzing. It is partly caused by an imbalance of nutrients other than Fe and supply of these is known to mitigate the toxicity. But the physiological and molecular mechanisms involved are unknown.MethodsWe investigated the effect of magnesium (Mg) on Fe toxicity tolerance in a field study in the Central Highlands of Madagascar and in hydroponic experiments with excess Fe (300 mg Fe L^-1). An RNA-seq analysis was conducted in a hydroponic experiment to elucidate possible mechanisms underlying Mg effects.Results and discussionAddition of Mg consistently decreased leaf bronzing under both field and hydroponic conditions, whereas potassium (K) addition caused minor effects. Plants treated with Mg tended to have smaller shoot Fe concentrations in the field, suggesting enhanced exclusion at the whole-plant level. However, analysis of multiple genotypes showed that Fe toxicity symptoms were also mitigated without a concomitant decrease of Fe concentration, suggesting that increased Mg supply confers tolerance at the tissue level. The hydroponic experiments also suggested that Mg mitigated leaf bronzing without significantly decreasing Fe concentration or oxidative stress as assessed by the content of malondialdehyde, a biomarker for oxidative stress. An RNA-seq analysis revealed that Mg induced more changes in leaves than roots. Subsequent cis-element analysis suggested that NAC transcription factor binding sites were enriched in genes induced by Fe toxicity in leaves. Addition of Mg caused non-significant enrichment of the same binding sites, suggesting that NAC family proteins may mediate the effect of Mg. This study provides clues for mitigating Fe toxicity-induced leaf bronzing in rice.

@ARTICLE{10.3389/fpls.2023.1213456,
AUTHOR={Rajonandraina, Toavintsoa and Ueda, Yoshiaki and Wissuwa, Matthias and Kirk, Guy J. D. and Rakotoson, Tovohery and Manwaring, Hanna and Andriamananjara, Andry and Razafimbelo, Tantely},
TITLE={Magnesium supply alleviates iron toxicity-induced leaf bronzing in rice through exclusion and tissue-tolerance mechanisms},
JOURNAL={Frontiers in Plant Science},
VOLUME={14},
YEAR={2023},
URL={https://www.frontiersin.org/articles/10.3389/fpls.2023.1213456},
DOI={10.3389/fpls.2023.1213456},
ISSN={1664-462X},
ABSTRACT={IntroductionIron (Fe) toxicity is a widespread nutritional disorder in lowland rice causing growth retardation and leaf symptoms referred to as leaf bronzing. It is partly caused by an imbalance of nutrients other than Fe and supply of these is known to mitigate the toxicity. But the physiological and molecular mechanisms involved are unknown.MethodsWe investigated the effect of magnesium (Mg) on Fe toxicity tolerance in a field study in the Central Highlands of Madagascar and in hydroponic experiments with excess Fe (300 mg Fe L^-1). An RNA-seq analysis was conducted in a hydroponic experiment to elucidate possible mechanisms underlying Mg effects.Results and discussionAddition of Mg consistently decreased leaf bronzing under both field and hydroponic conditions, whereas potassium (K) addition caused minor effects. Plants treated with Mg tended to have smaller shoot Fe concentrations in the field, suggesting enhanced exclusion at the whole-plant level. However, analysis of multiple genotypes showed that Fe toxicity symptoms were also mitigated without a concomitant decrease of Fe concentration, suggesting that increased Mg supply confers tolerance at the tissue level. The hydroponic experiments also suggested that Mg mitigated leaf bronzing without significantly decreasing Fe concentration or oxidative stress as assessed by the content of malondialdehyde, a biomarker for oxidative stress. An RNA-seq analysis revealed that Mg induced more changes in leaves than roots. Subsequent cis-element analysis suggested that NAC transcription factor binding sites were enriched in genes induced by Fe toxicity in leaves. Addition of Mg caused non-significant enrichment of the same binding sites, suggesting that NAC family proteins may mediate the effect of Mg. This study provides clues for mitigating Fe toxicity-induced leaf bronzing in rice.}
}

D. Qiu, E. Lange, T. M. Haas, I. Prucker, S. Masuda, Y. L. Wang, G. Felix, G. Schaaf, and H. J. Jessen, "Bacterial Pathogen Infection Triggers Magic Spot Nucleotide Signaling in Arabidopsis thaliana Chloroplasts through Specific RelA/SpoT Homologues," Journal of the American Chemical Society, vol. 145, iss. 29, pp. 16081-16089, 2023. doi:10.1021/jacs.3c04445
[BibTeX] [PDF]

@article{doi:10.1021/jacs.3c04445,
author = {Qiu, Danye and Lange, Esther and Haas, Thomas M. and Prucker, Isabel and Masuda, Shinji and Wang, Yan L. and Felix, Georg and Schaaf, Gabriel and Jessen, Henning J.},
title = {Bacterial Pathogen Infection Triggers Magic Spot Nucleotide Signaling in Arabidopsis thaliana Chloroplasts through Specific RelA/SpoT Homologues},
journal = {Journal of the American Chemical Society},
volume = {145},
number = {29},
pages = {16081-16089},
year = {2023},
doi = {10.1021/jacs.3c04445},
note ={PMID: 37437195},
URL = {https://doi.org/10.1021/jacs.3c04445},
eprint = {https://doi.org/10.1021/jacs.3c04445}
}

A. Bonerath, J. Haunert, J. S. B. Mitchell, and B. Niedermann, "Shortcut Hulls: Vertex-restricted Outer Simplifications of Polygons," Computational Geometry Theory and Applications, p. 101983, 2023. doi:10.1016/j.comgeo.2023.101983
[BibTeX] [PDF] [Video]

Let P be a polygon and C a set of shortcuts, where each shortcut is a directed straight-line segment connecting two vertices of P. A shortcut hull of P is another polygon that encloses P and whose oriented boundary is composed of elements from C. We require P and the output shortcut hull to be weakly simple polygons, which we define as a generalization of simple polygons. Shortcut hulls find their application in cartography, where a common task is to compute simplified representations of area features. We aim at a shortcut hull that has a small area and a small perimeter. Our optimization objective is to minimize a convex combination of these two criteria. If no holes in the shortcut hull are allowed, the problem admits a straight-forward solution via computation of shortest paths. For the more challenging case in which the shortcut hull may contain holes, we present a polynomial-time algorithm that is based on computing a constrained, weighted triangulation of the input polygon's exterior. We use this problem as a starting point for investigating further variants, e.g., restricting the number of edges or bends. We demonstrate that shortcut hulls can be used for the schematization of polygons.

@article{BONERATH2023101983,
abstract = {Let P be a polygon and C a set of shortcuts, where each shortcut is a directed straight-line segment connecting two vertices of P. A shortcut hull of P is another polygon that encloses P and whose oriented boundary is composed of elements from C. We require P and the output shortcut hull to be weakly simple polygons, which we define as a generalization of simple polygons. Shortcut hulls find their application in cartography, where a common task is to compute simplified representations of area features. We aim at a shortcut hull that has a small area and a small perimeter. Our optimization objective is to minimize a convex combination of these two criteria. If no holes in the shortcut hull are allowed, the problem admits a straight-forward solution via computation of shortest paths. For the more challenging case in which the shortcut hull may contain holes, we present a polynomial-time algorithm that is based on computing a constrained, weighted triangulation of the input polygon's exterior. We use this problem as a starting point for investigating further variants, e.g., restricting the number of edges or bends. We demonstrate that shortcut hulls can be used for the schematization of polygons.},
author = {Annika Bonerath and Jan-Henrik Haunert and Joseph S.B. Mitchell and Benjamin Niedermann},
doi = {10.1016/j.comgeo.2023.101983},
issn = {0925-7721},
journal = {Computational Geometry Theory and Applications},
pages = {101983},
title = {Shortcut Hulls: Vertex-restricted Outer Simplifications of Polygons},
url = {https://www.sciencedirect.com/science/article/pii/S0925772123000032},
videourl = {https://www.youtube.com/watch?v=vNImUU4nur8},
year = {2023}}

K. Gutbrod, J. Romer, and P. Dörmann, "Analysis of isoprenyl-phosphates by liquid chromatography-mass spectrometry," in Methods in Enzymology, Elsevier, 2023, vol. 683, p. 171–190. doi:10.1016/bs.mie.2022.08.026
[BibTeX]

AB - Isoprenoids in plants are synthesized following the plastidial methylerythritol-4-phosphate (MEP) pathway or the mevalonate pathway localized to the cytosol and peroxisomes. Isoprenyl-diphosphates (isoprenyl-PP) are important intermediates for the synthesis of chlorophyll, carotenoids, sterols, and other isoprenoids in plants. The quantification of isoprenyl-PP is challenging due to the amphipathic structure, the low abundance, and the susceptibility to hydrolysis during extraction and storage. Different methods for the measurement of isoprenyl-phosphates have been developed. Isoprenyl-phosphates can be measured after radioactive labeling or after derivatization. Liquid chromatography-mass spectrometry (LC-MS) methods provide enhanced sensitivity, but still require the extraction from large amounts of sample material. In the protocol presented here, the monophosphates and diphosphates of farnesol, geranylgeraniol and phytol are isolated from plant material with an isopropanol-containing buffer and quantified by LC-MS using citronellyl-P and citronellyl-PP as internal standards. With a low limit of detection for phytyl-P, geranylgeranyl-P, phytyl-PP, and geranylgeranyl-PP, isoprenyl-phosphates can be accurately measured in Arabidopsis leaves or seeds starting with only 20mg of fresh weight.

@incollection{gutbrod2023analysis,
title={Analysis of isoprenyl-phosphates by liquid chromatography-mass spectrometry},
author={Gutbrod, Katharina and Romer, Jill and D{\"o}rmann, Peter},
booktitle={Methods in Enzymology},
volume={683},
pages={171--190},
year={2023},
abstract={AB - Isoprenoids in plants are synthesized following the plastidial
methylerythritol-4-phosphate (MEP) pathway or the mevalonate pathway localized to
the cytosol and peroxisomes. Isoprenyl-diphosphates (isoprenyl-PP) are important
intermediates for the synthesis of chlorophyll, carotenoids, sterols, and other
isoprenoids in plants. The quantification of isoprenyl-PP is challenging due to
the amphipathic structure, the low abundance, and the susceptibility to
hydrolysis during extraction and storage. Different methods for the measurement
of isoprenyl-phosphates have been developed. Isoprenyl-phosphates can be measured
after radioactive labeling or after derivatization. Liquid chromatography-mass
spectrometry (LC-MS) methods provide enhanced sensitivity, but still require the
extraction from large amounts of sample material. In the protocol presented here,
the monophosphates and diphosphates of farnesol, geranylgeraniol and phytol are
isolated from plant material with an isopropanol-containing buffer and quantified
by LC-MS using citronellyl-P and citronellyl-PP as internal standards. With a low
limit of detection for phytyl-P, geranylgeranyl-P, phytyl-PP, and
geranylgeranyl-PP, isoprenyl-phosphates can be accurately measured in Arabidopsis
leaves or seeds starting with only 20mg of fresh weight.},
doi={10.1016/bs.mie.2022.08.026},
publisher={Elsevier}
}

M. H. ur Rahman, H. E. Ahrends, A. Raza, and T. Gaiser, "Current approaches for modeling ecosystem services and biodiversity in agroforestry systems: Challenges and ways forward," Frontiers in Forests and Global Change, vol. 5, 2023. doi:10.3389/ffgc.2022.1032442
[BibTeX] [PDF]

Limited modeling studies are available for the process-based simulation of ecosystem services (ESS) and biodiversity (BD) in agroforestry systems (AFS). To date, limited field scale AFs models are available to simulate all possible ESS and BD together. We conducted an extensive systematic review of available agroforestry (AF), BD, and soil erosion models for the simulation potential of seven most desirable ESS in AFS. Simple to complex AF models have an inherent limitation of being objective-specific. A few complex and dynamic AF models did not meet the recent interest and demands for the simulation of ESS under AFS. Further, many ESS modules especially soil erosion, GHGs emission, groundwater recharge, onsite water retention, nutrients and pesticide leaching, and BD are often missing in available AF models, while some existing soil erosion models can be used in combination with AF models. Likewise mechanistic and process-based BD diversity models are lacking or found limited simulation potential for ESS under AFS. However, further efforts of model development and improvement (integration and coupling) are needed for the better simulation of complex interactive processes belonging to ESS under AFS. There are different possibilities but a proficient modeling approach for better reliability, flexibility, and durability is to integrate and couple them into a process-based dynamic modular structure. Findings of the study further suggested that crop modeling frameworks (MFW) like SIMPLACE and APSIM could be potential ones for the integration and coupling of different suitable modeling approaches (AF, soil protection, GHGs emission, flood prevention, carbon sequestration, onsite water retention, ground recharge, nutrient leaching, and BD modules) in one platform for dynamic process based ESS estimation on daily basis at the field scale.

@ARTICLE{10.3389/ffgc.2022.1032442,
AUTHOR={Rahman, Muhammed Habib ur and Ahrends, Hella Ellen and Raza, Ahsan and Gaiser, Thomas},
TITLE={Current approaches for modeling ecosystem services and biodiversity in agroforestry systems: Challenges and ways forward},
JOURNAL={Frontiers in Forests and Global Change},
VOLUME={5},
YEAR={2023},
URL={https://www.frontiersin.org/articles/10.3389/ffgc.2022.1032442},
DOI={10.3389/ffgc.2022.1032442},
ISSN={2624-893X},
ABSTRACT={Limited modeling studies are available for the process-based simulation of ecosystem services (ESS) and biodiversity (BD) in agroforestry systems (AFS). To date, limited field scale AFs models are available to simulate all possible ESS and BD together. We conducted an extensive systematic review of available agroforestry (AF), BD, and soil erosion models for the simulation potential of seven most desirable ESS in AFS. Simple to complex AF models have an inherent limitation of being objective-specific. A few complex and dynamic AF models did not meet the recent interest and demands for the simulation of ESS under AFS. Further, many ESS modules especially soil erosion, GHGs emission, groundwater recharge, onsite water retention, nutrients and pesticide leaching, and BD are often missing in available AF models, while some existing soil erosion models can be used in combination with AF models. Likewise mechanistic and process-based BD diversity models are lacking or found limited simulation potential for ESS under AFS. However, further efforts of model development and improvement (integration and coupling) are needed for the better simulation of complex interactive processes belonging to ESS under AFS. There are different possibilities but a proficient modeling approach for better reliability, flexibility, and durability is to integrate and couple them into a process-based dynamic modular structure. Findings of the study further suggested that crop modeling frameworks (MFW) like SIMPLACE and APSIM could be potential ones for the integration and coupling of different suitable modeling approaches (AF, soil protection, GHGs emission, flood prevention, carbon sequestration, onsite water retention, ground recharge, nutrient leaching, and BD modules) in one platform for dynamic process based ESS estimation on daily basis at the field scale.}
}

K. Abdalla, T. Gaiser, S. J. Seidel, and J. Pausch, "Soil organic carbon and nitrogen in aggregates in response to over seven decades of farmyard manure application," Journal of Plant Nutrition and Soil Science, vol. 186, iss. 3, pp. 253-258, 2023. doi:10.1002/jpln.202300062
[BibTeX] [PDF]

Abstract The study aimed to evaluate the effects of long-term fertilisation on soil aggregation and the associated changes in soil organic carbon (SOC) and nitrogen (N) pools in aggregates. The combined application of mineral fertiliser and manure improved soil aggregation, SOC and N content in aggregates, compared to manure or mineral fertiliser alone, and thus proved to be a suitable fertilisation strategy to increase C sequestration in agroecosystems.

@article{https://doi.org/10.1002/jpln.202300062,
author = {Abdalla, Khatab and Gaiser, Thomas and Seidel, Sabine Julia and Pausch, Johanna},
title = {Soil organic carbon and nitrogen in aggregates in response to over seven decades of farmyard manure application},
journal = {Journal of Plant Nutrition and Soil Science},
volume = {186},
number = {3},
pages = {253-258},
keywords = {aggregates stability, agroecosystem, carbon sequestration, soil respiration},
doi = {10.1002/jpln.202300062},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/jpln.202300062},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/jpln.202300062},
abstract = {Abstract The study aimed to evaluate the effects of long-term fertilisation on soil aggregation and the associated changes in soil organic carbon (SOC) and nitrogen (N) pools in aggregates. The combined application of mineral fertiliser and manure improved soil aggregation, SOC and N content in aggregates, compared to manure or mineral fertiliser alone, and thus proved to be a suitable fertilisation strategy to increase C sequestration in agroecosystems.},
year = {2023}
}

E. I. Katche and A. S. Mason, "Resynthesized Rapeseed (Brassica napus): Breeding and Genomics," Critical Reviews in Plant Sciences, vol. 42, iss. 2, pp. 65-92, 2023. doi:10.1080/07352689.2023.2186021
[BibTeX]

@article{doi:10.1080/07352689.2023.2186021,
author = {Elizabeth Ihien Katche and Annaliese S. Mason},
title = {Resynthesized Rapeseed (Brassica napus): Breeding and Genomics},
journal = {Critical Reviews in Plant Sciences},
volume = {42},
number = {2},
pages = {65-92},
year = {2023},
publisher = {Taylor & Francis},
doi = {10.1080/07352689.2023.2186021},
}

M. Londres, M. Schmink, J. Börner, A. E. Duchelle, and G. P. Frey, "Multidimensional forests: Complexity of forest-based values and livelihoods across Amazonian socio-cultural and geopolitical contexts," World Development, vol. 165, p. 106200, 2023. doi:10.1016/j.worlddev.2023.106200
[BibTeX]

Research on the contribution of forests to local livelihoods has so far had a strong focus on quantifying the monetary value of forest-derived products and services. In this paper, we move beyond monetary valuation and integrate the less tangible and sometimes culturally complex dimensions through which forests support local livelihoods. We look at four local contexts in the Brazilian, Bolivian and Ecuadorian Amazon, which differ markedly in terms of their biophysical, sociocultural and geopolitical settings. Combining economic and anthropological data, we used quantitative and qualitative methods, and measures of the ecological impacts of local forest uses. Quantitative analyses drew on datasets from 48 communities, and 510 households, while the qualitative analyses relied on semi-structured interviews with 78 families in 22 communities. Forest-based livelihoods exhibited complex portfolios, diversified production systems, seasonal variation of activities, and different specialization strategies. Beyond a source of subsistence and cash incomes, forests were locally valued by people across all sites in terms of identities, worldviews, territorial attachment, governance, and conservation. Populations with a longer history of interactions with the environment displayed more complex forest-related cultural systems, but even among people who had migrated into the forest in a more recent historical period, forest-based self-cultural identification was evident. At all sites, forests were unanimously recognized as critical to people’s health and wellbeing, despite substantial differences in local histories, policy and market environments. The findings underscore the persistent importance of non-economic values of forests as both Indigenous and non-Indigenous groups constantly adapt their forest and land use practices based on transcultural exchange and changing conditions. A focus on economic value as the rationale for forest conservation disregards the striking resilience of cultural values in promoting forest conservation and use by diverse local and Indigenous communities, especially when supported by favorable policies and markets.

@article{LONDRES2023106200,
title = {Multidimensional forests: Complexity of forest-based values and livelihoods across Amazonian socio-cultural and geopolitical contexts},
journal = {World Development},
volume = {165},
pages = {106200},
year = {2023},
issn = {0305-750X},
doi = {10.1016/j.worlddev.2023.106200},
author = {Marina Londres and Marianne Schmink and Jan Börner and Amy E. Duchelle and Gabriel Ponzoni Frey},
keywords = {Cultural values, Forest identity, Conservation behavior, Brazil, Bolivia, Ecuador},
abstract = {Research on the contribution of forests to local livelihoods has so far had a strong focus on quantifying the monetary value of forest-derived products and services. In this paper, we move beyond monetary valuation and integrate the less tangible and sometimes culturally complex dimensions through which forests support local livelihoods. We look at four local contexts in the Brazilian, Bolivian and Ecuadorian Amazon, which differ markedly in terms of their biophysical, sociocultural and geopolitical settings. Combining economic and anthropological data, we used quantitative and qualitative methods, and measures of the ecological impacts of local forest uses. Quantitative analyses drew on datasets from 48 communities, and 510 households, while the qualitative analyses relied on semi-structured interviews with 78 families in 22 communities. Forest-based livelihoods exhibited complex portfolios, diversified production systems, seasonal variation of activities, and different specialization strategies. Beyond a source of subsistence and cash incomes, forests were locally valued by people across all sites in terms of identities, worldviews, territorial attachment, governance, and conservation. Populations with a longer history of interactions with the environment displayed more complex forest-related cultural systems, but even among people who had migrated into the forest in a more recent historical period, forest-based self-cultural identification was evident. At all sites, forests were unanimously recognized as critical to people’s health and wellbeing, despite substantial differences in local histories, policy and market environments. The findings underscore the persistent importance of non-economic values of forests as both Indigenous and non-Indigenous groups constantly adapt their forest and land use practices based on transcultural exchange and changing conditions. A focus on economic value as the rationale for forest conservation disregards the striking resilience of cultural values in promoting forest conservation and use by diverse local and Indigenous communities, especially when supported by favorable policies and markets.}
}

K. Alsafadi, S. Bi, H. G. Abdo, H. Almohamad, B. Alatrach, A. K. Srivastava, M. Al-Mutiry, S. K. Bal, M. Chandran, and S. Mohammed, "Modeling the impacts of projected climate change on wheat crop suitability in semi-arid regions using the AHP-based weighted climatic suitability index and CMIP6," Geoscience Letters, vol. 10, iss. 1, p. 1–21, 2023. doi:10.1186/s40562-023-00273-y
[BibTeX] [PDF]

Due to rapid population growth and the limitation of land resources, the sustainability of agricultural ecosystems has attracted more attention all over the world. Human activities will alter the components of the atmosphere and lead to climate change, which consequently affects crop production badly. In this context, wheat is considered an important crop and ranks as one of the top strategic crops globally. The main objective of this research is to develop a new approach (a weighted climatic suitability index) for evaluating the climate suitability for wheat production. The specific objectives are to project the impact of future climate change on wheat suitability using three models based on WCSI and CMIP6-based projections and to identify the most vulnerable area to climate change and productivity reduction. The climatic criteria for wheat production were selected and classified into eight indicators based on the Sys' scheme and the FAO framework, and then the weighted overlay approach was used in conjunction with the analytic hierarchy process. To confirm the reliability of the integrated WCSI, we determined the nonlinear curve fitting of integrated WCSI-induced wheat yields by the exponential growth equation. Finally, the CMIP6-GCMs projected from three shared socioeconomic pathways were used for WCSI mapping and predicting wheat yields in the short and long term (Southern Syria was selected as a case study). The results show that the nonlinear correlation between wheat yields and the integrated WCSI was 0.78 (R2 = 0.61) confirming the integrated WCSI's reliability in reflecting yield variation caused by climate suitability. The results indicated that WCSI for wheat will be lower over the study area during 2080–2100 compared to the current climate. During 2080–2100, the wheat yield is projected to decrease by 0.2–0.8 t. ha−1 in the western parts of the study area. The findings of this study could be used to plan and develop adaptation strategies for sustainable wheat production in the face of projected climate change. The results of the study will also help in the strategic planning of wheat production in Syria under the projected climate. The results of this research are limited to small areas as a case study, although they are not relevant to similar regions worldwide. However, the study employs novel analytical methods that can be used broadly.} url={https://doi.org/10.1186/s40562-023-00273-y

@article{alsafadi2023modeling,
title={Modeling the impacts of projected climate change on wheat crop suitability in semi-arid regions using the AHP-based weighted climatic suitability index and CMIP6},
author={Alsafadi, Karam and Bi, Shuoben and Abdo, Hazem Ghassan and Almohamad, Hussein and Alatrach, Basma and Srivastava, Amit Kumar and Al-Mutiry, Motrih and Bal, Santanu Kumar and Chandran, MA and Mohammed, Safwan},
journal={Geoscience Letters},
volume={10},
number={1},
pages={1--21},
url={https://geoscienceletters.springeropen.com/articles/10.1186/s40562-023-00273-y},
year={2023},
abstract={Due to rapid population growth and the limitation of land resources, the sustainability of agricultural ecosystems has attracted more attention all over the world. Human activities will alter the components of the atmosphere and lead to climate change, which consequently affects crop production badly. In this context, wheat is considered an important crop and ranks as one of the top strategic crops globally. The main objective of this research is to develop a new approach (a weighted climatic suitability index) for evaluating the climate suitability for wheat production. The specific objectives are to project the impact of future climate change on wheat suitability using three models based on WCSI and CMIP6-based projections and to identify the most vulnerable area to climate change and productivity reduction. The climatic criteria for wheat production were selected and classified into eight indicators based on the Sys' scheme and the FAO framework, and then the weighted overlay approach was used in conjunction with the analytic hierarchy process. To confirm the reliability of the integrated WCSI, we determined the nonlinear curve fitting of integrated WCSI-induced wheat yields by the exponential growth equation. Finally, the CMIP6-GCMs projected from three shared socioeconomic pathways were used for WCSI mapping and predicting wheat yields in the short and long term (Southern Syria was selected as a case study). The results show that the nonlinear correlation between wheat yields and the integrated WCSI was 0.78 (R2 = 0.61) confirming the integrated WCSI's reliability in reflecting yield variation caused by climate suitability. The results indicated that WCSI for wheat will be lower over the study area during 2080–2100 compared to the current climate. During 2080–2100, the wheat yield is projected to decrease by 0.2–0.8 t. ha−1 in the western parts of the study area. The findings of this study could be used to plan and develop adaptation strategies for sustainable wheat production in the face of projected climate change. The results of the study will also help in the strategic planning of wheat production in Syria under the projected climate. The results of this research are limited to small areas as a case study, although they are not relevant to similar regions worldwide. However, the study employs novel analytical methods that can be used broadly.}
url={https://doi.org/10.1186/s40562-023-00273-y},
doi={10.1186/s40562-023-00273-y},
publisher={Springer}
}

D. Uhlig, A. E. Berns, B. Wu, and W. Amelung, "Mean nutrient uptake depths of cereal crops change with compost incorporation into subsoil–evidence from 87Sr/86Sr ratios," Plant and Soil, p. 1–16, 2023. doi:10.1007/s11104-023-06047-x
[BibTeX] [PDF]

AB - Root restricting layers often hinder crops from accessing the large reservoir of bioavailable mineral nutrients situated in subsoil. This study aims to explore changes in the mean nutrient uptake depth of cereal crops when removing root restricting layers through subsoil management.

@article{uhlig2023mean,
title={Mean nutrient uptake depths of cereal crops change with compost incorporation into subsoil--evidence from 87Sr/86Sr ratios},
author={Uhlig, David and Berns, Anne E and Wu, Bei and Amelung, Wulf},
journal={Plant and Soil},
pages={1--16},
url={https://doi.org/10.1007/s11104-023-06047-x},
doi={10.1007/s11104-023-06047-x},
year={2023},
abstract={AB - Root restricting layers often hinder crops from accessing the large reservoir of bioavailable mineral nutrients situated in subsoil. This study aims to explore changes in the mean nutrient uptake depth of cereal crops when removing root restricting layers through subsoil management.},
publisher={Springer}
}

P. Martre, S. Dueri, J. Guarin, F. Ewert, H. Webber, D. Calderini, G. Molero, M. Reynolds, D. Miralles, G. García, H. Brown, M. George, R. Craigie, J. Cohan, J. Deswarte, G. Slafer, F. Giunta, D. Cammarano, R. Ferrise, and A. Srivastava, "The nitrogen price of improved wheat yield under climate change," Research Square, 2023. doi:10.21203/rs.3.rs-2667076/v1
[BibTeX] [PDF]

@article{Martrenitrogen,
author = {Martre, Pierre and Dueri, Sibylle and Guarin, Jose and Ewert, Frank and Webber, Heidi and Calderini, Daniel and Molero, Gemma and Reynolds, Matthew and Miralles, Daniel and García, Guillermo and Brown, Hamish and George, Mike and Craigie, Rob and Cohan, Jean-Pierre and Deswarte, Jean-Charles and Slafer, Gustavo and Giunta, Francesco and Cammarano, Davide and Ferrise, Roberto and Srivastava, Amit},
year = {2023},
month = {03},
pages = {},
journal = {Research Square},
url ={https://www.researchsquare.com/article/rs-2667076/v1},
title = {The nitrogen price of improved wheat yield under climate change},
doi = {10.21203/rs.3.rs-2667076/v1}
}

L. E. Skadell, F. Schneider, M. I. Gocke, J. Guigue, W. Amelung, S. L. Bauke, E. U. Hobley, D. Barkusky, B. Honermeier, I. Kögel-Knabner, U. Schmidhalter, K. Schweitzer, S. J. Seidel, S. Siebert, M. Sommer, Y. Vaziritabar, and A. Don, "Twenty percent of agricultural management effects on organic carbon stocks occur in subsoils – Results of ten long-term experiments," Agriculture, Ecosystems & Environment, vol. 356, p. 108619, 2023. doi:10.1016/j.agee.2023.108619
[BibTeX] [PDF]

Agricultural management can influence soil organic carbon (SOC) stocks and thus may contribute to carbon sequestration and climate change mitigation. The soil depth to which agricultural management practices affect SOC is uncertain. Soil depth may have an important bearing on soil carbon dynamics, so it is important to consider depth effects to capture fully changes in SOC stocks. This applies in particular to the evaluation of carbon farming measures, which are becoming increasingly important due to climate change. We sampled and analysed the upper metre of mineral cropland soils from ten long-term experiments (LTEs) in Germany to quantify depth-specific effects on SOC stocks of common agricultural management practices: mineral nitrogen (N) fertilisation, a combination of N, phosphorus (P) and potassium (K) fertilisation, irrigation, a crop rotation with preceding crops (pre-crops), straw incorporation, application of farmyard manure (FYM), liming, and reduced tillage. In addition, the effects of soil compaction on SOC stocks were examined as a negative side effect of agricultural management. Results showed that 19 ± 3 % of total management effects on SOC stocks were found in the upper subsoil (30–50 cm) and 3 ± 4 % in the lower subsoil (50–100 cm), including all agricultural management practices with significant topsoil SOC effects, while 79 ± 7 % of management effects were in the topsoil (0–30 cm). Nitrogen and NPK fertilisation were the treatments that had the greatest effect on subsoil organic carbon (OC) stocks, followed by irrigation, FYM application and straw incorporation. Sampling down to a depth of 50 cm resulted in significantly higher SOC effects than when considering topsoil only. A crop rotation with pre-crops, liming, reduced tillage and soil compaction did not significantly affect SOC stocks at any depth increment. Since approximately 20 % of the impact of agricultural management on SOC stocks occurs in the subsoil, we recommend soil monitoring programs and carbon farming schemes extend their standard soil sampling down to 50 cm depth to capture fully agricultural management effects on SOC.

@article{SKADELL2023108619,
title = {Twenty percent of agricultural management effects on organic carbon stocks occur in subsoils – Results of ten long-term experiments},
journal = {Agriculture, Ecosystems & Environment},
volume = {356},
pages = {108619},
year = {2023},
issn = {0167-8809},
doi = {10.1016/j.agee.2023.108619},
url = {https://www.sciencedirect.com/science/article/pii/S0167880923002785},
author = {Laura E. Skadell and Florian Schneider and Martina I. Gocke and Julien Guigue and Wulf Amelung and Sara L. Bauke and Eleanor U. Hobley and Dietmar Barkusky and Bernd Honermeier and Ingrid Kögel-Knabner and Urs Schmidhalter and Kathlin Schweitzer and Sabine J. Seidel and Stefan Siebert and Michael Sommer and Yavar Vaziritabar and Axel Don},
keywords = {Carbon farming, Carbon sequestration, Croplands, Long-term experiments, Soil carbon, Soil depth},
abstract = {Agricultural management can influence soil organic carbon (SOC) stocks and thus may contribute to carbon sequestration and climate change mitigation. The soil depth to which agricultural management practices affect SOC is uncertain. Soil depth may have an important bearing on soil carbon dynamics, so it is important to consider depth effects to capture fully changes in SOC stocks. This applies in particular to the evaluation of carbon farming measures, which are becoming increasingly important due to climate change. We sampled and analysed the upper metre of mineral cropland soils from ten long-term experiments (LTEs) in Germany to quantify depth-specific effects on SOC stocks of common agricultural management practices: mineral nitrogen (N) fertilisation, a combination of N, phosphorus (P) and potassium (K) fertilisation, irrigation, a crop rotation with preceding crops (pre-crops), straw incorporation, application of farmyard manure (FYM), liming, and reduced tillage. In addition, the effects of soil compaction on SOC stocks were examined as a negative side effect of agricultural management. Results showed that 19 ± 3 % of total management effects on SOC stocks were found in the upper subsoil (30–50 cm) and 3 ± 4 % in the lower subsoil (50–100 cm), including all agricultural management practices with significant topsoil SOC effects, while 79 ± 7 % of management effects were in the topsoil (0–30 cm). Nitrogen and NPK fertilisation were the treatments that had the greatest effect on subsoil organic carbon (OC) stocks, followed by irrigation, FYM application and straw incorporation. Sampling down to a depth of 50 cm resulted in significantly higher SOC effects than when considering topsoil only. A crop rotation with pre-crops, liming, reduced tillage and soil compaction did not significantly affect SOC stocks at any depth increment. Since approximately 20 % of the impact of agricultural management on SOC stocks occurs in the subsoil, we recommend soil monitoring programs and carbon farming schemes extend their standard soil sampling down to 50 cm depth to capture fully agricultural management effects on SOC.}
}

R. S. de Nóia Júnior, J. Deswarte, J. Cohan, P. Martre, M. van Der Velde, R. Lecerf, H. Webber, F. Ewert, A. C. Ruane, G. A. Slafer, and others, "The extreme 2016 wheat yield failure in France," Global Change Biology, vol. 29, iss. 11, p. 3130–3146, 2023. doi:10.1111/gcb.16662
[BibTeX] [PDF]

@article{noia2023extreme,
title={The extreme 2016 wheat yield failure in France},
author={N{\'o}ia J{\'u}nior, Rog{\'e}rio de S and Deswarte, Jean-Charles and Cohan, Jean-Pierre and Martre, Pierre and van Der Velde, Marijn and Lecerf, Remi and Webber, Heidi and Ewert, Frank and Ruane, Alex C and Slafer, Gustavo A and others},
journal={Global Change Biology},
volume={29},
number={11},
doi={10.1111/gcb.16662},
url={https://onlinelibrary.wiley.com/doi/10.1111/gcb.16662},
pages={3130--3146},
year={2023},
publisher={Wiley Online Library}
}

A. K. Srivastava, F. Ewert, A. S. Akinwumiju, W. Zeng, A. Ceglar, K. S. Ezui, A. Adelodun, A. Adebayo, J. Sobamowo, M. Singh, and others, "Cassava yield gap—A model-based assessment in Nigeria," Frontiers in Sustainable Food Systems, vol. 6, p. 1058775, 2023. doi:10.3389/fsufs.2022.1058775
[BibTeX] [PDF]

@article{srivastava2023cassava,
title={Cassava yield gap—A model-based assessment in Nigeria},
author={Srivastava, Amit Kumar and Ewert, Frank and Akinwumiju, Akinola Shola and Zeng, Wenzhi and Ceglar, Andrej and Ezui, Kodjovi Senam and Adelodun, Adedeji and Adebayo, Abass and Sobamowo, Jumoke and Singh, Manmeet and others},
journal={Frontiers in Sustainable Food Systems},
volume={6},
pages={1058775},
year={2023},
doi={10.3389/fsufs.2022.1058775},
url={https://doi.org/10.3389/fsufs.2022.1058775},
publisher={Frontiers}
}

D. Burger, S. Bauke, W. Amelung, and M. Sommer, "Fast agricultural topsoil re-formation after complete topsoil loss–Evidence from a unique historical field experiment," Geoderma, vol. 434, p. 116492, 2023. doi:10.1016/j.geoderma.2023.116492
[BibTeX] [PDF]

@article{burger2023fast,
title={Fast agricultural topsoil re-formation after complete topsoil loss--Evidence from a unique historical field experiment},
author={Burger, DJ and Bauke, SL and Amelung, W and Sommer, M},
journal={Geoderma},
volume={434},
pages={116492},
doi={10.1016/j.geoderma.2023.116492},
url={https://doi.org/10.1016/j.geoderma.2023.116492},
year={2023},
publisher={Elsevier}
}

J. Gao, W. Zeng, Z. Ren, C. Ao, G. Lei, T. Gaiser, and A. K. Srivastava, "A Fertilization Decision Model for Maize, Rice, and Soybean Based on Machine Learning and Swarm Intelligent Search Algorithms," Agronomy, vol. 13, iss. 5, p. 1400, 2023. doi:10.3390/agronomy13051400
[BibTeX] [PDF]

@article{gao2023fertilization,
title={A Fertilization Decision Model for Maize, Rice, and Soybean Based on Machine Learning and Swarm Intelligent Search Algorithms},
author={Gao, Jian and Zeng, Wenzhi and Ren, Zhipeng and Ao, Chang and Lei, Guoqing and Gaiser, Thomas and Srivastava, Amit Kumar},
journal={Agronomy},
volume={13},
doi={10.3390/agronomy13051400},
url={https://doi.org/10.3390/agronomy13051400},
number={5},
pages={1400},
year={2023},
publisher={MDPI}
}

S. Sanow, W. Kuang, G. Schaaf, P. Huesgen, U. Schurr, U. Roessner, M. Watt, and B. Arsova, "Molecular mechanisms of Pseudomonas assisted plant nitrogen uptake: opportunities for modern agriculture," Molecular Plant-Microbe Interactions, 2023. doi:10.1094/MPMI-10-22-0223-CR
[BibTeX] [PDF]

@article{sanow2023molecular,
title={Molecular mechanisms of Pseudomonas assisted plant nitrogen uptake: opportunities for modern agriculture},
author={Sanow, Stefan and Kuang, Weiqi and Schaaf, Gabriel and Huesgen, Pitter and Schurr, Ulrich and Roessner, Ute and Watt, Michelle and Arsova, Borjana},
journal={Molecular Plant-Microbe Interactions},
year={2023},
doi={10.1094/MPMI-10-22-0223-CR },
url={https://apsjournals.apsnet.org/doi/10.1094/MPMI-10-22-0223-CR},
publisher={Am Phytopath Society}
}

V. Sentek, A. Velescu, W. Wilcke, C. Henke, N. Peters, G. Welp, and W. Amelung, "Nitrogen release from different polymer-coated urea fertilizers in soil is affected by soil properties," Soil Use and Management, 2023. doi:10.1111/sum.12905
[BibTeX] [PDF]

@article{sentek2023nitrogen,
title={Nitrogen release from different polymer-coated urea fertilizers in soil is affected by soil properties},
author={Sentek, Valerie and Velescu, Andre and Wilcke, Wolfgang and Henke, Catarina and Peters, Nils and Welp, Gerd and Amelung, Wulf},
journal={Soil Use and Management},
year={2023},
doi={10.1111/sum.12905},
url={https://doi.org/10.1111/sum.12905},
publisher={Wiley Online Library}
}

A. Schnepf, C. K. Black, V. Couvreur, B. M. Delory, C. Doussan, A. Heymans, M. Javaux, D. Khare, A. Koch, T. Koch, and others, "Collaborative benchmarking of functional-structural root architecture models: Quantitative comparison of simulated root water uptake," in silico Plants, p. diad005, 2023. doi:10.1093/insilicoplants/diad005
[BibTeX] [PDF]

@article{schnepf2023collaborative,
title={Collaborative benchmarking of functional-structural root architecture models: Quantitative comparison of simulated root water uptake},
author={Schnepf, Andrea and Black, Christopher K and Couvreur, Valentin and Delory, Benjamin M and Doussan, Claude and Heymans, Adrien and Javaux, Mathieu and Khare, Deepanshu and Koch, Axelle and Koch, Timo and others},
journal={in silico Plants},
pages={diad005},
doi={10.1093/insilicoplants/diad005},
url={https://doi.org/10.1093/insilicoplants/diad005},
year={2023},
publisher={Oxford University Press UK}
}

R. A. Sikora, J. Helder, L. P. Molendijk, J. Desaeger, S. Eves-van den Akker, and A. Mahlein, "Integrated Nematode Management in a World in Transition: Constraints, Policy, Processes, and Technologies for the Future," Annual Review of Phytopathology, vol. 61, 2023. doi:10.1146/annurev-phyto-021622-113058
[BibTeX] [PDF]

@article{sikora2023integrated,
title={Integrated Nematode Management in a World in Transition: Constraints, Policy, Processes, and Technologies for the Future},
author={Sikora, Richard A and Helder, Johannes and Molendijk, Leendert PG and Desaeger, Johan and Eves-van den Akker, Sebastian and Mahlein, Anne-Katrin},
journal={Annual Review of Phytopathology},
volume={61},
year={2023},
doi={10.1146/annurev-phyto-021622-113058},
url={https://doi.org/10.1146/annurev-phyto-021622-113058},
publisher={Annual Reviews}
}

B. Jost, D. Coopmann, C. Holst, and H. Kuhlmann, "Real movement or systematic errors? – TLS-based deformation analysis of a concrete wall," Journal of Applied Geodesy, vol. 17, iss. 2, p. 139–149, 2023. doi:10.1515/jag-2022-0041
[BibTeX] [PDF]

@article{jost2023real,
title={Real movement or systematic errors? -- TLS-based deformation analysis of a concrete wall},
author={Jost, Berit and Coopmann, Daniel and Holst, Christoph and Kuhlmann, Heiner},
journal={Journal of Applied Geodesy},
volume={17},
number={2},
pages={139--149},
year={2023},
doi={10.1515/jag-2022-0041},
url={https://www.phenorob.de/wp-content/uploads/2024/08/G-2023_Jost_JAG.pdf},
year={2023},
publisher={De Gruyter}
}

J. Kierdorf and R. Roscher, "Reliability Scores From Saliency Map Clusters for Improved Image-Based Harvest-Readiness Prediction in Cauliflower," IEEE Geoscience and Remote Sensing Letters, vol. 20, pp. 1-5, 2023. doi:10.1109/LGRS.2023.3293802
[BibTeX] [PDF]

@ARTICLE{10177737,
author={Kierdorf, Jana and Roscher, Ribana},
journal={IEEE Geoscience and Remote Sensing Letters},
title={Reliability Scores From Saliency Map Clusters for Improved Image-Based Harvest-Readiness Prediction in Cauliflower},
year={2023},
volume={20},
number={},
pages={1-5},
keywords={Reliability;Head;Predictive models;Data models;Crops;Computational modeling;Training;Harvest prediction;interpretability;reliability;saliency mapping;spectral clustering (SC)},
doi={10.1109/LGRS.2023.3293802},
url={https://ieeexplore.ieee.org/document/10177737},
}

G. M. de Oliveira, J. Sellare, and J. Börner, "Mind your language: Political signaling deforestation in the Brazilian Amazon," ZEF–Discussion Papers on Development Policy, iss. 326, p. 34, 2023. doi:10.2139/ssrn.4380343
[BibTeX] [PDF]

@article{oliveira2023mind,
title={Mind your language: Political signaling deforestation in the Brazilian Amazon},
author={Oliveira, Gustavo Magalh{\~a}es de and Sellare, Jorge and B{\"o}rner, Jan},
journal={ZEF--Discussion Papers on Development Policy},
number={326},
url={https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4380343},
doi={10.2139/ssrn.4380343},
pages={34},
year={2023}
}

J. Weyler, T. Läbe, F. Magistri, J. Behley, and C. Stachniss, "Towards Domain Generalization in Crop and Weed Segmentation for Precision Farming Robots," IEEE Robotics and Automation Letters, vol. 8, iss. 6, pp. 3310-3317, 2023. doi:10.1109/LRA.2023.3262417
[BibTeX] [PDF] [Code]

@ARTICLE{10083238,
author={Weyler, Jan and Läbe, Thomas and Magistri, Federico and Behley, Jens and Stachniss, Cyrill},
journal={IEEE Robotics and Automation Letters},
title={Towards Domain Generalization in Crop and Weed Segmentation for Precision Farming Robots},
year={2023},
volume={8},
number={6},
codeurl={https://github.com/PRBonn/DG-CWS},
url={https://www.researchgate.net/profile/Cyrill_Stachniss/publication/369589307_Towards_Domain_Generalization_in_Crop_and_Weed_Segmentation_for_Precision_Farming_Robots/links/64467f368ac1946c7a49ea37/Towards-Domain-Generalization-in-Crop-and-Weed-Segmentation-for-Precision-Farming-Robots.pdf},
pages={3310-3317},
doi={10.1109/LRA.2023.3262417}}

E. Marks, M. Sodano, F. Magistri, L. Wiesmann, D. Desai, R. Marcuzzi, J. Behley, and C. Stachniss, "High Precision Leaf Instance Segmentation for Phenotyping in Point Clouds Obtained Under Real Field Conditions," IEEE Robotics and Automation Letters, pp. 1-8, 2023. doi:10.1109/LRA.2023.3288383
[BibTeX] [PDF] [Code] [Video]

@ARTICLE{10158793,
author={Marks, Elias and Sodano, Matteo and Magistri, Federico and Wiesmann, Louis and Desai, Dhagash and Marcuzzi, Rodrigo and Behley, Jens and Stachniss, Cyrill},
journal={IEEE Robotics and Automation Letters},
title={High Precision Leaf Instance Segmentation for Phenotyping in Point Clouds Obtained Under Real Field Conditions},
year={2023},
volume={},
number={},
pages={1-8},
codeurl={https://github.com/PRBonn/plant_pcd_segmenter},
videourl={https://www.youtube.com/watch?v=dvA1SvQ4iEY},
url={https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/marks2023ral.pdf},
doi={10.1109/LRA.2023.3288383}}

T. Daum, F. Baudron, R. Birner, M. Qaim, and I. Grass, "Addressing agricultural labour issues is key to biodiversity-smart farming," Biological Conservation, vol. 284, p. 110165, 2023. doi:10.1016/j.biocon.2023.110165
[BibTeX] [PDF]

There is an urgent need for agricultural development strategies that reconcile agricultural production and biodiversity conservation. This is especially true in the Global South where population growth is rapid and much of the world's remaining biodiversity is located. Combining conceptual thoughts with empirical insights from case studies in Indonesia and Ethiopia, we argue that such strategies will have to pay more attention to agricultural labour dynamics. Farmers have a strong motivation to reduce the heavy toil associated with farming by adopting technologies that save labour but can negatively affect biodiversity. Labour constraints can also prevent farmers from adopting technologies that improve biodiversity but increase labour intensity. Without explicitly accounting for labour issues, conservation efforts can hardly be successful. We hence highlight the need for biodiversity-smart agriculture, that is farming practices or systems that reconcile biodiversity with land and labour productivity. Our empirical insights suggest that technological and institutional options to reconcile farmers' socio-economic goals and biodiversity conservation exist but that more needs to be done to implement such options at scale.

@article{DAUM2023110165,
title = {Addressing agricultural labour issues is key to biodiversity-smart farming},
journal = {Biological Conservation},
volume = {284},
pages = {110165},
year = {2023},
issn = {0006-3207},
doi = {10.1016/j.biocon.2023.110165},
url = {https://www.sciencedirect.com/science/article/pii/S0006320723002665},
author = {Thomas Daum and Frédéric Baudron and Regina Birner and Matin Qaim and Ingo Grass},
keywords = {Biodiversity conservation, Agricultural development, Sustainability, Land-sharing, Trade-offs, Labour, Africa, Indonesia},
abstract = {There is an urgent need for agricultural development strategies that reconcile agricultural production and biodiversity conservation. This is especially true in the Global South where population growth is rapid and much of the world's remaining biodiversity is located. Combining conceptual thoughts with empirical insights from case studies in Indonesia and Ethiopia, we argue that such strategies will have to pay more attention to agricultural labour dynamics. Farmers have a strong motivation to reduce the heavy toil associated with farming by adopting technologies that save labour but can negatively affect biodiversity. Labour constraints can also prevent farmers from adopting technologies that improve biodiversity but increase labour intensity. Without explicitly accounting for labour issues, conservation efforts can hardly be successful. We hence highlight the need for biodiversity-smart agriculture, that is farming practices or systems that reconcile biodiversity with land and labour productivity. Our empirical insights suggest that technological and institutional options to reconcile farmers' socio-economic goals and biodiversity conservation exist but that more needs to be done to implement such options at scale.}
}

Y. L. Chong, J. Weyler, P. Lottes, J. Behley, and C. Stachniss, "Unsupervised Generation of Labeled Training Images for Crop-Weed Segmentation in New Fields and on Different Robotic Platforms," IEEE Robotics and Automation Letters (RA-L), 2023.
[BibTeX] [PDF] [Code] [Video]

@article{chong2023ral,
author = {Y.L. Chong and J. Weyler and P. Lottes and J. Behley and C. Stachniss},
title = {{Unsupervised Generation of Labeled Training Images for Crop-Weed
Segmentation in New Fields and on Different Robotic Platforms}},
journal = {IEEE Robotics and Automation Letters (RA-L)},
volume = {},
number = {},
pages = {},
year = 2023,
issn = {},
doi = {},
codeurl = {https://github.com/PRBonn/StyleGenForLabels},
videourl = {https://www.youtube.com/watch?v=SpvrR9sgf2k},
url={https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/chong2023ral.pdf},
note = {accepted}
}

L. Lobefaro, M. V. R. Malladi, O. Vysotska, T. Guadagnino, and C. Stachniss, "Estimating 4D Data Associations Towards Spatial-Temporal Mapping of Growing Plants for Agricultural Robots," in Proc. of the IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS) , 2023.
[BibTeX] [PDF] [Code] [Video]

@inproceedings{lobefaro2023iros,
author = {L. Lobefaro and M.V.R. Malladi and O. Vysotska and T. Guadagnino and C. Stachniss},
title = {{Estimating 4D Data Associations Towards Spatial-Temporal Mapping of Growing Plants for Agricultural Robots}},
booktitle = {Proc. of the IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS)},
year = {2023},
codeurl = {https://github.com/PRBonn/plants_temporal_matcher},
videourl = {https://www.youtube.com/watch?v=HpJPIzmXoag},
URL = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/lobefaro2023iros.pdf},
}

Y. Pan, F. Magistri, T. Läbe, E. Marks, C. Smitt, C. S. McCool, J. Behley, and C. Stachniss, "Panoptic Mapping with Fruit Completion and Pose Estimation for Horticultural Robots," in Proc. of the IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS) , 2023. doi:10.48550/arXiv.2303.08923
[BibTeX] [PDF] [Code] [Video]

@inproceedings{pan2023iros,
author = {Y. Pan and F. Magistri and T. L\"abe and E. Marks and C. Smitt and C.S. McCool and J. Behley and C. Stachniss},
title = {{Panoptic Mapping with Fruit Completion and Pose Estimation for Horticultural Robots}},
booktitle = {Proc. of the IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS)},
year = {2023},
codeurl = {https://github.com/PRBonn/HortiMapping},
Videourl = {https://www.youtube.com/watch?v=fSyHBhskjqA},
url={https://arxiv.org/abs/2303.08923},
doi={10.48550/arXiv.2303.08923},
note = {accepted}
}

A. Enders, M. Vianna, T. Gaiser, G. Krauss, H. Webber, A. K. Srivastava, S. J. Seidel, A. Tewes, E. E. Rezaei, and F. Ewert, "SIMPLACE - A versatile modelling and simulation framework for sustainable crops and agroecosystems," in silico Plants, 2023. doi:10.1093/insilicoplants/diad006
[BibTeX] [PDF] [Video]

{Agricultural system analysis has considerably evolved over the last years, allowing scientists to quantify complex interactions in crops and agroecosystems. Computer-based models have become a central tool for such analysis, using formulated mathematical representations (algorithms) of different biophysical processes to simulate complex system behaviour. Nevertheless, the current large variety of algorithms in combination with non-standardization in their use limits rapid and rigorous model improvement and testing. This is particularly important because contextualization is a key aspect used to formulate the appropriate model structure for a specific research question, framing a clear demand for “next generation” models being modular and flexible. This paper aims to describe the Scientific Impact assessment and Modelling PLatform for Advanced Crop and Ecosystem management (SIMPLACE), which has been developed over the last decade to address the various aforementioned issues and support appropriate model formulations and interoperability. We describe its main technical implementation and features to develop customized model solutions that can be applied to a number of cropping systems with high flexibility, performance and transparency. A brief review of exemplary applications of SIMPLACE is provided covering the different topics, crops and cropping systems, spatial scales, and geographies. We stress that standardized documentation of modules, variables ontology, and data archives are key requirements to maintain and assist model development, and reproducibility. The increasing demand for more complex diversified and integrated production systems (e.g., intercropping, livestock-grazing, agroforestry) and the associated impacts on sustainable food systems also require the strong collaboration of a multidisciplinary community of modellers and stakeholders.}

@article{10.1093/insilicoplants/diad006,
author = {Enders, Andreas and Vianna, Murilo and Gaiser, Thomas and Krauss, Gunther and Webber, Heidi and Srivastava, Amit Kumar and Seidel, Sabine Julia and Tewes, Andreas and Rezaei, Ehsan Eyshi and Ewert, Frank},
title = "{SIMPLACE - A versatile modelling and simulation framework for sustainable crops and agroecosystems}",
journal = {in silico Plants},
year = {2023},
month = {05},
abstract = "{Agricultural system analysis has considerably evolved over the last years, allowing scientists to quantify complex interactions in crops and agroecosystems. Computer-based models have become a central tool for such analysis, using formulated mathematical representations (algorithms) of different biophysical processes to simulate complex system behaviour. Nevertheless, the current large variety of algorithms in combination with non-standardization in their use limits rapid and rigorous model improvement and testing. This is particularly important because contextualization is a key aspect used to formulate the appropriate model structure for a specific research question, framing a clear demand for “next generation” models being modular and flexible. This paper aims to describe the Scientific Impact assessment and Modelling PLatform for Advanced Crop and Ecosystem management (SIMPLACE), which has been developed over the last decade to address the various aforementioned issues and support appropriate model formulations and interoperability. We describe its main technical implementation and features to develop customized model solutions that can be applied to a number of cropping systems with high flexibility, performance and transparency. A brief review of exemplary applications of SIMPLACE is provided covering the different topics, crops and cropping systems, spatial scales, and geographies. We stress that standardized documentation of modules, variables ontology, and data archives are key requirements to maintain and assist model development, and reproducibility. The increasing demand for more complex diversified and integrated production systems (e.g., intercropping, livestock-grazing, agroforestry) and the associated impacts on sustainable food systems also require the strong collaboration of a multidisciplinary community of modellers and stakeholders.}",
issn = {2517-5025},
doi = {10.1093/insilicoplants/diad006},
videourl = {https://www.youtube.com/watch?v=kj6TXkxocCg},
url = {https://doi.org/10.1093/insilicoplants/diad006},
note = {diad006},
eprint = {https://academic.oup.com/insilicoplants/advance-article-pdf/doi/10.1093/insilicoplants/diad006/50424261/diad006.pdf},
}

L. Shang, J. Wang, D. Schäfer, T. Heckelei, J. Gall, F. Appel, and H. Storm, "Surrogate modelling of a detailed farm-level model using deep learning," Journal of Agricultural Economics, 2023. doi:10.1111/1477-9552.12543
[BibTeX] [PDF]

Abstract Technological change co-determines agri-environmental performance and farm structural transformation. Meaningful impact assessment of related policies can be derived from farm-level models that are rich in technology details and environmental indicators, integrated with agent-based models capturing dynamic farm interaction. However, such integration faces considerable challenges affecting model development, debugging and computational demands in application. Surrogate modelling using deep learning techniques can facilitate such integration for simulations with broad regional coverage. We develop surrogates of the farm model FarmDyn using different architectures of neural networks. Our specifically designed evaluation metrics allow practitioners to assess trade-offs among model fit, inference time and data requirements. All tested neural networks achieve a high fit but differ substantially in inference time. The Multilayer Perceptron shows almost top performance in all criteria but saves strongly on inference time compared to a Bi-directional Long Short Term Memory.

@article{https://doi.org/10.1111/1477-9552.12543,
author = {Shang, Linmei and Wang, Jifeng and Schäfer, David and Heckelei, Thomas and Gall, Juergen and Appel, Franziska and Storm, Hugo},
title = {Surrogate modelling of a detailed farm-level model using deep learning},
journal = {Journal of Agricultural Economics},
year = {2023},
keywords = {agent-based model, deep learning, farm modelling, neural networks, surrogate model, upscaling},
doi = {10.1111/1477-9552.12543},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/1477-9552.12543},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/1477-9552.12543},
abstract = {Abstract Technological change co-determines agri-environmental performance and farm structural transformation. Meaningful impact assessment of related policies can be derived from farm-level models that are rich in technology details and environmental indicators, integrated with agent-based models capturing dynamic farm interaction. However, such integration faces considerable challenges affecting model development, debugging and computational demands in application. Surrogate modelling using deep learning techniques can facilitate such integration for simulations with broad regional coverage. We develop surrogates of the farm model FarmDyn using different architectures of neural networks. Our specifically designed evaluation metrics allow practitioners to assess trade-offs among model fit, inference time and data requirements. All tested neural networks achieve a high fit but differ substantially in inference time. The Multilayer Perceptron shows almost top performance in all criteria but saves strongly on inference time compared to a Bi-directional Long Short Term Memory.}
}

M. K. Gerullis and W. Schulz, "Robustness of plant breeding systems under automated phenotyping," Smart Agricultural Technology, vol. 5, p. 100225, 2023. doi:10.1016/j.atech.2023.100225
[BibTeX] [PDF]

Automated phenotyping is hailed to transform modern agricultural systems and relieve many sustainability challenges, like maintaining food security, halting biodiversity loss, and adapting to climate change. Yet, these issues can be traced from farming back to plant breeding and highly depend on the crop genetic diversity in use. Engineering and plant science usually take a look at automated phenotyping from a technical perspective and value its merits for research in plant breeding. In contrast, we lay out a more holistic view and ask what the social-ecological-technical repercussions to the robustness of on-site crop genetic diversity are from laboratory to breeding nursery where varieties for farming are being produced. We argue that automated phenotyping has a twofold impact on systemic robustness. On the one hand, it improves adaptive capacity by accelerating the breeding process. On the other hand, it's implementation can destabilize the system and have unforeseen negative impacts on on-site crop genetic diversity. Therefore, we call for explicit monitoring of the possible side effects by the system's governance.

@article{GERULLIS2023100225,
title = {Robustness of plant breeding systems under automated phenotyping},
journal = {Smart Agricultural Technology},
volume = {5},
pages = {100225},
year = {2023},
issn = {2772-3755},
doi = {10.1016/j.atech.2023.100225},
url = {https://www.sciencedirect.com/science/article/pii/S2772375523000552?via%3Dihub },
author = {Maria Katharina Gerullis and Wiebke Schulz},
keywords = {Automated phenotyping, Infrastructures, Technology adoption, Coupled infrastructure framework, Crop genetic diversity},
abstract = {Automated phenotyping is hailed to transform modern agricultural systems and relieve many sustainability challenges, like maintaining food security, halting biodiversity loss, and adapting to climate change. Yet, these issues can be traced from farming back to plant breeding and highly depend on the crop genetic diversity in use. Engineering and plant science usually take a look at automated phenotyping from a technical perspective and value its merits for research in plant breeding. In contrast, we lay out a more holistic view and ask what the social-ecological-technical repercussions to the robustness of on-site crop genetic diversity are from laboratory to breeding nursery where varieties for farming are being produced. We argue that automated phenotyping has a twofold impact on systemic robustness. On the one hand, it improves adaptive capacity by accelerating the breeding process. On the other hand, it's implementation can destabilize the system and have unforeseen negative impacts on on-site crop genetic diversity. Therefore, we call for explicit monitoring of the possible side effects by the system's governance.}
}

L. Shang, C. Pahmeyer, T. Heckelei, S. Rasch, and H. Storm, "How much can farmers pay for weeding robots? A Monte Carlo simulation study," Precision Agriculture, p. 1–26, 2023. doi:10.1007/s11119-023-10015-x
[BibTeX] [PDF]

This paper investigates the Maximum Acquisition Values (MAVs) of weeding robots and their determinants in both organic and conventional sugar beet farming in Germany. The MAV is defined in this paper as the price of the weeding robot that renders the same net profit as the current weeding methods. For our analysis, a Monte Carlo simulation approach is used, combined with empirical data and data collected from weeding robot companies. The results show that the MAVs of mechanical weeding robots for organic farming are substantially higher than that of spot spraying robots for conventional farming. Technology attributes are more influential than labour cost in determining the MAVs of weeding robots: in organic farming, technology attributes such as area capacity and weeding efficiency impact the MAVs of mechanical weeding robots the most; in conventional farming, supervision intensity and the robot’s ability to save herbicides are the most influential factors. The wage rate of unskilled labour, relevant for manual weeding, plays a more important role in determining the MAVs than that of skilled labour, relevant for supervision of the robot. This implies that a shortage of seasonal workers and hence increases in the wage of low-skilled labour could be important drivers of the adoption of mechanical weeding robots. Plot characteristics such as plot size and mechanisation level only have limited impacts on the MAVs.

@article{shangprecision,
author={Shang, Linmei and Pahmeyer, Christoph and Heckelei, Thomas and Rasch, Sebastian and Storm, Hugo},
year={2023},
pages={1--26},
title={How much can farmers pay for weeding robots? A Monte Carlo simulation study},
journal={Precision Agriculture},
abstract={This paper investigates the Maximum Acquisition Values (MAVs) of weeding robots and their determinants in both organic and conventional sugar beet farming in Germany. The MAV is defined in this paper as the price of the weeding robot that renders the same net profit as the current weeding methods. For our analysis, a Monte Carlo simulation approach is used, combined with empirical data and data collected from weeding robot companies. The results show that the MAVs of mechanical weeding robots for organic farming are substantially higher than that of spot spraying robots for conventional farming. Technology attributes are more influential than labour cost in determining the MAVs of weeding robots: in organic farming, technology attributes such as area capacity and weeding efficiency impact the MAVs of mechanical weeding robots the most; in conventional farming, supervision intensity and the robot’s ability to save herbicides are the most influential factors. The wage rate of unskilled labour, relevant for manual weeding, plays a more important role in determining the MAVs than that of skilled labour, relevant for supervision of the robot. This implies that a shortage of seasonal workers and hence increases in the wage of low-skilled labour could be important drivers of the adoption of mechanical weeding robots. Plot characteristics such as plot size and mechanisation level only have limited impacts on the MAVs.},
doi={10.1007/s11119-023-10015-x},
url={https://link.springer.com/article/10.1007/s11119-023-10015-x}}

N. Nause, F. R. Ispizua Yamati, M. Seidel, A. Mahlein, and C. M. Hoffmann, "Workflow for phenotyping sugar beet roots by automated evaluation of cell characteristics and tissue arrangement using digital image processing," Plant Methods, vol. 19, 2023. doi:10.1186/s13007-023-01014-0
[BibTeX] [PDF]

Cell characteristics, including cell type, size, shape, packing, cell-to-cell-adhesion, intercellular space, and cell wall thickness, influence the physical characteristics of plant tissues. Genotypic differences were found concerning damage susceptibility related to beet texture for sugar beet (Beta vulgaris). Sugar beet storage roots are characterized by heterogeneous tissue with several cambium rings surrounded by small-celled vascular tissue and big-celled sugar-storing parenchyma between the rings. This study presents a procedure for phenotyping heterogeneous tissues like beetroots by imaging.

@article{nauseplantmenthods,
author={Nause, Nelia and Ispizua Yamati, Facundo R. and Seidel, Marion and Mahlein, Anne-Katrin and Hoffmann, Christa M.},
year = {2023},
title = {Workflow for phenotyping sugar beet roots by automated evaluation of cell characteristics and tissue arrangement using digital image processing},
journal = {Plant Methods},
abstract = {Cell characteristics, including cell type, size, shape, packing, cell-to-cell-adhesion, intercellular space, and cell wall thickness, influence the physical characteristics of plant tissues. Genotypic differences were found concerning damage susceptibility related to beet texture for sugar beet (Beta vulgaris). Sugar beet storage roots are characterized by heterogeneous tissue with several cambium rings surrounded by small-celled vascular tissue and big-celled sugar-storing parenchyma between the rings. This study presents a procedure for phenotyping heterogeneous tissues like beetroots by imaging.},
volume = {19},
issue = {1},
doi = {10.1186/s13007-023-01014-0},
url = {https://plantmethods.biomedcentral.com/articles/10.1186/s13007-023-01014-0}}

P. Zimmer, M. Halstead, and C. McCool, "Panoptic One-Click Segmentation: Applied to Agricultural Data," IEEE Robotics and Automation Letters, vol. 8, iss. 5, pp. 2478-2485, 2023. doi:10.1109/LRA.2023.3254451
[BibTeX] [PDF] [Code]

@ARTICLE{10064096,
author={Zimmer, Patrick and Halstead, Michael and McCool, Chris},
journal={IEEE Robotics and Automation Letters},
title={Panoptic One-Click Segmentation: Applied to Agricultural Data},
year={2023},
volume={8},
number={5},
pages={2478-2485},
codeurl={https://github.com/Agricultural-Robotics-Bonn/UniBonn-Agrobot-PanopticOneClick},
url={https://arxiv.org/pdf/2303.08689.pdf},
doi={10.1109/LRA.2023.3254451}}

S. Solgi, S. H. Ahmadi, and S. J. Seidel, "Remote sensing of canopy water status of the irrigated winter wheat fields and the paired anomaly analyses on the spectral vegetation indices and grain yields," Agricultural Water Management, vol. 280, p. 108226, 2023. doi:10.1016/j.agwat.2023.108226
[BibTeX] [PDF]

Agriculture is the world's largest consumer of freshwater resources, particularly in semi-arid regions where crop production is reliant on both irrigation and rainfall. Therefore, proper irrigation management is critical in achieving sustainable agriculture by increasing crop yield while conserving water resources. Remote sensing has demonstrated a great promise in monitoring crop status including crop water status based on the spectral vegetation index (VI). Therefore, the vegetation growth (normalized difference vegetation index, NDVI), vegetation water status (shortwave crop reflectance index, SCRI), and vegetation drought stress (moisture stress index, MSI) indices were calculated to assess the impact of irrigation management on canopy water status in a cluster of winter wheat fields in a semi-arid area in three non-consecutive growing seasons with different seasonal rainfall amounts and distributions. The winter wheat fields were typically irrigated six times in each growing season according to a fixed phenological-based irrigation scheduling. Results showed that NDVI, SCRI, and MSI had a high correlation with the remotely sensed locally calibrated leaf area index (LAI), among which NDVI had the strongest correlation (r = 0.9). The analysis revealed that the combined use of VIs succeeded in detecting spatial and temporal crop drought stress levels during the growing seasons. Furthermore, the normalized difference water index (NDWI) was interpreted to quantitatively classify the level and extent of drought stress in the winter wheat fields. The results of the NDWI analysis revealed that 62%, 100%, and 72% of the irrigated winter wheat fields have experienced some levels of drought stress during the normal growing season with wet spring, normal growing season with wet autumn, and dry growing season, respectively. The drought stress was basically due to the lack of effective rainfall during spring in March and April when crops have the highest vegetative growth, irrespective of the rainfall amount during autumn and winter. This revealed the importance of timely irrigation management during spring time. In addition, paired anomaly analysis of the NDVI, MSI, and SCRI with the wheat grain yields could identify good and poor wheat fields and recognize proper management zones of the wheat fields in terms of potential grain production. The findings of this study demonstrated that remote sensing is a strong and reliable tool in irrigation management to help sustain food production in arid or semi-arid areas with limited water resources.

@article{SOLGI2023108226,
title = {Remote sensing of canopy water status of the irrigated winter wheat fields and the paired anomaly analyses on the spectral vegetation indices and grain yields},
journal = {Agricultural Water Management},
volume = {280},
pages = {108226},
year = {2023},
issn = {0378-3774},
doi = {10.1016/j.agwat.2023.108226},
url = {https://www.researchgate.net/publication/368663196_Remote_sensing_of_canopy_water_status_of_the_irrigated_winter_wheat_fields_and_the_paired_anomaly_analyses_on_the_spectral_vegetation_indices_and_grain_yields},
author = {Shahin Solgi and Seyed Hamid Ahmadi and Sabine Julia Seidel},
keywords = {Irrigation management, Drought stress, Anomaly analysis, Spectral vegetation indices, Wheat grain yield, Leaf area index},
abstract = {Agriculture is the world's largest consumer of freshwater resources, particularly in semi-arid regions where crop production is reliant on both irrigation and rainfall. Therefore, proper irrigation management is critical in achieving sustainable agriculture by increasing crop yield while conserving water resources. Remote sensing has demonstrated a great promise in monitoring crop status including crop water status based on the spectral vegetation index (VI). Therefore, the vegetation growth (normalized difference vegetation index, NDVI), vegetation water status (shortwave crop reflectance index, SCRI), and vegetation drought stress (moisture stress index, MSI) indices were calculated to assess the impact of irrigation management on canopy water status in a cluster of winter wheat fields in a semi-arid area in three non-consecutive growing seasons with different seasonal rainfall amounts and distributions. The winter wheat fields were typically irrigated six times in each growing season according to a fixed phenological-based irrigation scheduling. Results showed that NDVI, SCRI, and MSI had a high correlation with the remotely sensed locally calibrated leaf area index (LAI), among which NDVI had the strongest correlation (r = 0.9). The analysis revealed that the combined use of VIs succeeded in detecting spatial and temporal crop drought stress levels during the growing seasons. Furthermore, the normalized difference water index (NDWI) was interpreted to quantitatively classify the level and extent of drought stress in the winter wheat fields. The results of the NDWI analysis revealed that 62%, 100%, and 72% of the irrigated winter wheat fields have experienced some levels of drought stress during the normal growing season with wet spring, normal growing season with wet autumn, and dry growing season, respectively. The drought stress was basically due to the lack of effective rainfall during spring in March and April when crops have the highest vegetative growth, irrespective of the rainfall amount during autumn and winter. This revealed the importance of timely irrigation management during spring time. In addition, paired anomaly analysis of the NDVI, MSI, and SCRI with the wheat grain yields could identify good and poor wheat fields and recognize proper management zones of the wheat fields in terms of potential grain production. The findings of this study demonstrated that remote sensing is a strong and reliable tool in irrigation management to help sustain food production in arid or semi-arid areas with limited water resources.}
}

C. Hubert, G. Steyns, T. Kraska, K. Luhmer, M. D. Moll, and R. Pude, "Essential oil content and physiological response of Mentha genotypes under different UV-treatments," Acta Horticulturae, vol. 1358, pp. 319-326, 2023. doi:10.17660/ActaHortic.2023.1358.41
[BibTeX] [PDF]

@article{huberthortic,
author = {Hubert, C and Steyns, G and Kraska, T and Luhmer, K and Moll, M.D. and Pude, R},
title = {Essential oil content and physiological response of Mentha genotypes under different UV-treatments},
journal = {Acta Horticulturae},
volume = {1358},
pages = {319-326},
year = {2023},
doi = {10.17660/ActaHortic.2023.1358.41},
url = {https://www.actahort.org/books/1358/1358_41.htm}
}

M. N. Siddiqui, K. Pandey, S. K. Bhadhury, B. Sadeqi, M. Schneider, M. Sanchez-Garcia, B. Stich, G. Schaaf, J. Léon, and A. Ballvora, "Convergently selected NPF2.12 coordinates root growth and nitrogen use efficiency in wheat and barley," New Phytologist, vol. n/a, iss. n/a, 2023. doi:10.1111/nph.18820
[BibTeX] [PDF]

Summary Understanding the genetic and molecular function of nitrate sensing and acquisition across crop species will accelerate breeding of cultivars with improved nitrogen use efficiency (NUE). Here, we performed a genome-wide scan using wheat and barley accessions characterized under low and high N inputs that uncovered the NPF2.12 gene, encoding a homolog of the Arabidopsis nitrate transceptor NRT1.6 and other low-affinity nitrate transporters that belong to the MAJOR FACILITATOR SUPERFAMILY. Next, it is shown that variations in the NPF2.12 promoter correlated with altered NPF2.12 transcript levels where decreased gene expression was measured under low nitrate availability. Multiple field trials revealed a significantly enhanced N content in leaves and grains and NUE in the presence of the elite allele TaNPF2.12TT grown under low N conditions. Further, the nitrate reductase encoding gene NIA1 was upregulated in npf2.12 mutant upon low nitrate concentrations, thereby resulting in elevated levels of nitric oxide (NO) production. This increase in NO correlated with the higher root growth, nitrate uptake and N translocation observed in the mutant when compared to wild-type. The presented data indicate that the elite haplotype alleles of NPF2.12 are convergently selected in wheat and barley that by inactivation indirectly contribute to root growth and NUE by activating NO signaling under low nitrate conditions.

@article{https://doi.org/10.1111/nph.18820,
author = {Siddiqui, Md. Nurealam and Pandey, Kailash and Bhadhury, Suzan Kumer and Sadeqi, Bahman and Schneider, Michael and Sanchez-Garcia, Miguel and Stich, Benjamin and Schaaf, Gabriel and Léon, Jens and Ballvora, Agim},
title = {Convergently selected NPF2.12 coordinates root growth and nitrogen use efficiency in wheat and barley},
journal = {New Phytologist},
volume = {n/a},
number = {n/a},
year = {2023},
pages = {},
keywords = {cereals, genetic variation, genome-wide association mapping, nitrate transport, nitrogen use efficiency, root system architecture},
doi = {10.1111/nph.18820},
url = {https://nph.onlinelibrary.wiley.com/doi/abs/10.1111/nph.18820},
eprint = {https://nph.onlinelibrary.wiley.com/doi/pdf/10.1111/nph.18820},
abstract = {Summary Understanding the genetic and molecular function of nitrate sensing and acquisition across crop species will accelerate breeding of cultivars with improved nitrogen use efficiency (NUE). Here, we performed a genome-wide scan using wheat and barley accessions characterized under low and high N inputs that uncovered the NPF2.12 gene, encoding a homolog of the Arabidopsis nitrate transceptor NRT1.6 and other low-affinity nitrate transporters that belong to the MAJOR FACILITATOR SUPERFAMILY. Next, it is shown that variations in the NPF2.12 promoter correlated with altered NPF2.12 transcript levels where decreased gene expression was measured under low nitrate availability. Multiple field trials revealed a significantly enhanced N content in leaves and grains and NUE in the presence of the elite allele TaNPF2.12TT grown under low N conditions. Further, the nitrate reductase encoding gene NIA1 was upregulated in npf2.12 mutant upon low nitrate concentrations, thereby resulting in elevated levels of nitric oxide (NO) production. This increase in NO correlated with the higher root growth, nitrate uptake and N translocation observed in the mutant when compared to wild-type. The presented data indicate that the elite haplotype alleles of NPF2.12 are convergently selected in wheat and barley that by inactivation indirectly contribute to root growth and NUE by activating NO signaling under low nitrate conditions.}
}

E. Alisaac and A. Mahlein, "Fusarium Head Blight on Wheat: Biology, Modern Detection and Diagnosis and Integrated Disease Management," Toxins, vol. 15, iss. 3, 2023. doi:10.3390/toxins15030192
[BibTeX] [PDF]

Fusarium head blight (FHB) is a major threat for wheat production worldwide. Most reviews focus on Fusarium graminearum as a main causal agent of FHB. However, different Fusarium species are involved in this disease complex. These species differ in their geographic adaptation and mycotoxin profile. The incidence of FHB epidemics is highly correlated with weather conditions, especially rainy days with warm temperatures at anthesis and an abundance of primary inoculum. Yield losses due to the disease can reach up to 80% of the crop. This review summarizes the Fusarium species involved in the FHB disease complex with the corresponding mycotoxin profiles, disease cycle, diagnostic methods, the history of FHB epidemics, and the management strategy of the disease. In addition, it discusses the role of remote sensing technology in the integrated management of the disease. This technology can accelerate the phenotyping process in the breeding programs aiming at FHB-resistant varieties. Moreover, it can support the decision-making strategies to apply fungicides via monitoring and early detection of the diseases under field conditions. It can also be used for selective harvest to avoid mycotoxin-contaminated plots in the field.

@Article{toxins15030192,
AUTHOR = {Alisaac, Elias and Mahlein, Anne-Katrin},
TITLE = {Fusarium Head Blight on Wheat: Biology, Modern Detection and Diagnosis and Integrated Disease Management},
JOURNAL = {Toxins},
VOLUME = {15},
YEAR = {2023},
NUMBER = {3},
ARTICLE-NUMBER = {192},
URL = {https://www.mdpi.com/2072-6651/15/3/192},
ISSN = {2072-6651},
ABSTRACT = {Fusarium head blight (FHB) is a major threat for wheat production worldwide. Most reviews focus on Fusarium graminearum as a main causal agent of FHB. However, different Fusarium species are involved in this disease complex. These species differ in their geographic adaptation and mycotoxin profile. The incidence of FHB epidemics is highly correlated with weather conditions, especially rainy days with warm temperatures at anthesis and an abundance of primary inoculum. Yield losses due to the disease can reach up to 80% of the crop. This review summarizes the Fusarium species involved in the FHB disease complex with the corresponding mycotoxin profiles, disease cycle, diagnostic methods, the history of FHB epidemics, and the management strategy of the disease. In addition, it discusses the role of remote sensing technology in the integrated management of the disease. This technology can accelerate the phenotyping process in the breeding programs aiming at FHB-resistant varieties. Moreover, it can support the decision-making strategies to apply fungicides via monitoring and early detection of the diseases under field conditions. It can also be used for selective harvest to avoid mycotoxin-contaminated plots in the field.},
DOI = {10.3390/toxins15030192}
}

V. Sushko, D. Zhang, J. Gall, and A. Khoreva, "One-Shot Synthesis of Images and Segmentation Masks," in IEEE/CVF Winter Conference on Applications of Computer Vision , 2023, pp. 6274-6283. doi:10.1109/WACV56688.2023.00622
[BibTeX] [PDF] [Code] [Video]

@inproceedings{inproceedings,
author = {Sushko, Vadim and Zhang, Dan and Gall, Juergen and Khoreva, Anna},
year = {2023},
month = {01},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
pages = {6274-6283},
title = {One-Shot Synthesis of Images and Segmentation Masks},
codeurl = {https://github.com/boschresearch/one-shot-synthesis},
Videourl = {https://www.youtube.com/watch?v=isBgfJrwhEg},
url={https://openaccess.thecvf.com/content/WACV2023/papers/Sushko_One-Shot_Synthesis_of_Images_and_Segmentation_Masks_WACV_2023_paper.pdf},
doi = {10.1109/WACV56688.2023.00622}
}

F. Esser, L. Klingbeil, L. Zabawa, and H. Kuhlmann, "Quality Analysis of a High-Precision Kinematic Laser Scanning System for the Use of Spatio-Temporal Plant and Organ-Level Phenotyping in the Field," Remote Sensing, vol. 15, iss. 4, 2023. doi:10.3390/rs15041117
[BibTeX] [PDF]

Computing large-scale phenotypic traits[...]

@Article{rs15041117,
AUTHOR = {Esser, Felix and Klingbeil, Lasse and Zabawa, Lina and Kuhlmann, Heiner},
TITLE = {Quality Analysis of a High-Precision Kinematic Laser Scanning System for the Use of Spatio-Temporal Plant and Organ-Level Phenotyping in the Field},
JOURNAL = {Remote Sensing},
VOLUME = {15},
YEAR = {2023},
NUMBER = {4},
ARTICLE-NUMBER = {1117},
URL = {https://www.mdpi.com/2072-4292/15/4/1117},
ISSN = {2072-4292},
ABSTRACT = {Computing large-scale phenotypic traits[...]},
DOI = {10.3390/rs15041117}
}

G. Tombrink, A. Dreier, L. Klingbeil, and H. Kuhlmann, "Trajectory evaluation using repeated rail-bound measurements," Journal of Applied Geodesy, 2023. doi:doi:10.1515/jag-2022-0027
[BibTeX] [PDF]

@article{TombrinkDreierKlingbeilKuhlmann+2023,
url = {https://www.phenorob.de/wp-content/uploads/2024/08/2023_Tombrink_JAG_TrajectoryEval.pdf},
title = {Trajectory evaluation using repeated rail-bound measurements},
author = {Gereon Tombrink and Ansgar Dreier and Lasse Klingbeil and Heiner Kuhlmann},
journal = {Journal of Applied Geodesy},
doi = {doi:10.1515/jag-2022-0027},
year = {2023},
publisher={De Gruyter},
lastchecked = {2023-02-22}
}

C. Montzka, M. Donat, R. Raj, P. Welter, and J. S. Bates, "Sensitivity of LiDAR Parameters to Aboveground Biomass in Winter Spelt," Drones, vol. 7, iss. 2, 2023. doi:10.3390/drones7020121
[BibTeX] [PDF]

Information about the current biomass state of crops is important to evaluate whether the growth conditions are adequate in terms of water and nutrient supply to determine if there is need to react to diseases and to predict the expected yield. Passive optical Unmanned Aerial Vehicle (UAV)-based sensors such as RGB or multispectral cameras are able to sense the canopy surface and record, e.g., chlorophyll-related plant characteristics, which are often indirectly correlated to aboveground biomass. However, direct measurements of the plant structure can be provided by LiDAR systems. In this study, different LiDAR-based parameters are evaluated according to their relationship to aboveground fresh and dry biomass (AGB) for a winter spelt experimental field in Dahmsdorf, Brandenburg, Germany. The parameters crop height, gap fraction, and LiDAR intensity are analyzed according to their individual correlation with AGB, and also a multiparameter analysis using the Ordinary Least Squares Regression (OLS) is performed. Results indicate high absolute correlations of AGB with gap fraction and crop height (−0.82 and 0.77 for wet and −0.70 and 0.66 for dry AGB, respectively), whereas intensity needs further calibration or processing before it can be adequately used to estimate AGB (−0.27 and 0.22 for wet and dry AGB, respectively). An important outcome of this study is that the combined utilization of all LiDAR parameters via an OLS analysis results in less accurate AGB estimation than with gap fraction or crop height alone. Moreover, future AGB states in June and July were able to be estimated from May LiDAR parameters with high accuracy, indicating stable spatial patterns in crop characteristics over time.

@Article{drones7020121,
AUTHOR = {Montzka, Carsten and Donat, Marco and Raj, Rahul and Welter, Philipp and Bates, Jordan Steven},
TITLE = {Sensitivity of LiDAR Parameters to Aboveground Biomass in Winter Spelt},
JOURNAL = {Drones},
VOLUME = {7},
YEAR = {2023},
NUMBER = {2},
ARTICLE-NUMBER = {121},
URL = {https://www.mdpi.com/2504-446X/7/2/121},
ISSN = {2504-446X},
ABSTRACT = {Information about the current biomass state of crops is important to evaluate whether the growth conditions are adequate in terms of water and nutrient supply to determine if there is need to react to diseases and to predict the expected yield. Passive optical Unmanned Aerial Vehicle (UAV)-based sensors such as RGB or multispectral cameras are able to sense the canopy surface and record, e.g., chlorophyll-related plant characteristics, which are often indirectly correlated to aboveground biomass. However, direct measurements of the plant structure can be provided by LiDAR systems. In this study, different LiDAR-based parameters are evaluated according to their relationship to aboveground fresh and dry biomass (AGB) for a winter spelt experimental field in Dahmsdorf, Brandenburg, Germany. The parameters crop height, gap fraction, and LiDAR intensity are analyzed according to their individual correlation with AGB, and also a multiparameter analysis using the Ordinary Least Squares Regression (OLS) is performed. Results indicate high absolute correlations of AGB with gap fraction and crop height (−0.82 and 0.77 for wet and −0.70 and 0.66 for dry AGB, respectively), whereas intensity needs further calibration or processing before it can be adequately used to estimate AGB (−0.27 and 0.22 for wet and dry AGB, respectively). An important outcome of this study is that the combined utilization of all LiDAR parameters via an OLS analysis results in less accurate AGB estimation than with gap fraction or crop height alone. Moreover, future AGB states in June and July were able to be estimated from May LiDAR parameters with high accuracy, indicating stable spatial patterns in crop characteristics over time.},
DOI = {10.3390/drones7020121}
}

M. Sodano, F. Magistri, T. Guadagnino, J. Behley, and C. Stachniss, "Robust Double-Encoder Network for RGB-D Panoptic Segmentation," in Proc.~of the IEEE Intl.~Conf.~on Robotics & Automation (ICRA) , 2023. doi:10.1109/icra48891.2023.10160315
[BibTeX] [PDF] [Code] [Video]

@inproceedings{sodano2023icra,
author = {Matteo Sodano and Federico Magistri and Tiziano Guadagnino and Jens Behley and Cyrill Stachniss},
title = {{Robust Double-Encoder Network for RGB-D Panoptic Segmentation}},
booktitle = {Proc.~of the IEEE Intl.~Conf.~on Robotics & Automation (ICRA)},
year = {2023},
url = {https://arxiv.org/pdf/2210.02834.pdf},
doi= {10.1109/icra48891.2023.10160315},
videourl = {https://www.youtube.com/watch?v=r1pabV3sQYk},
codeurl = {https://github.com/PRBonn/PS-res-excite},
}

I. Vizzo, T. Guadagnino, B. Mersch, L. Wiesmann, J. Behley, and C. Stachniss, "KISS-ICP: In Defense of Point-to-Point ICP – Simple, Accurate, and Robust Registration If Done the Right Way," IEEE Robotics and Automation Letters, vol. 8, iss. 2, pp. 1-8, 2023. doi:10.1109/LRA.2023.3236571
[BibTeX] [PDF] [Code] [Video]

@article{vizzo2023ral,
author = {Vizzo, Ignacio and Guadagnino, Tiziano and Mersch, Benedikt and Wiesmann, Louis and Behley, Jens and Stachniss, Cyrill},
title = {{KISS-ICP: In Defense of Point-to-Point ICP -- Simple, Accurate, and Robust Registration If Done the Right Way}},
journal = {IEEE Robotics and Automation Letters},
pages = {1-8},
doi = {10.1109/LRA.2023.3236571},
url={https://arxiv.org/pdf/2209.15397.pdf},
volume = {8},
number = {2},
year = {2023},
videourl = {https://www.youtube.com/watch?v=h71aGiD-uxU},
codeurl = {https://github.com/PRBonn/kiss-icp},
}

M. Splietker and S. Behnke, "Rendering the Directional TSDF for Tracking and Multi-Sensor Registration with Point-To-Plane Scale ICP," Robotics and Autonomous Systems, vol. 162, p. 104337, 2023. doi:10.1016/j.robot.2022.104337
[BibTeX] [PDF]

Dense real-time tracking and mapping from RGB-D images is an important tool for many robotic applications, such as navigation and manipulation. The recently presented Directional Truncated Signed Distance Function (DTSDF) is an augmentation of the regular TSDF that shows potential for more coherent maps and improved tracking performance. In this work, we present methods for rendering depth- and color images from the DTSDF, making it a true drop-in replacement for the regular TSDF in established trackers. We evaluate the algorithm on well-established datasets and observe that our method improves tracking performance and increases re-usability of mapped scenes. Furthermore, we add color integration which notably improves color-correctness at adjacent surfaces. Our novel formulation of combined ICP with frame-to-keyframe photometric error minimization further improves tracking results. Lastly, we introduce Sim(3) point-to-plane ICP for refining pose priors in a multi-sensor scenario with different scale factors.

@article{SPLIETKER2023104337,
title = {Rendering the Directional TSDF for Tracking and Multi-Sensor Registration with Point-To-Plane Scale ICP},
journal = {Robotics and Autonomous Systems},
volume = {162},
pages = {104337},
year = {2023},
issn = {0921-8890},
doi = {10.1016/j.robot.2022.104337},
url = {https://www.ais.uni-bonn.de/papers/RAS_2023_Splietker.pdf},
author = {Malte Splietker and Sven Behnke},
keywords = {SLAM, TSDF, Surface orientation, ICP, Frame-to-keyframe, Point-to-plane (3)},
abstract = {Dense real-time tracking and mapping from RGB-D images is an important tool for many robotic applications, such as navigation and manipulation. The recently presented Directional Truncated Signed Distance Function (DTSDF) is an augmentation of the regular TSDF that shows potential for more coherent maps and improved tracking performance. In this work, we present methods for rendering depth- and color images from the DTSDF, making it a true drop-in replacement for the regular TSDF in established trackers. We evaluate the algorithm on well-established datasets and observe that our method improves tracking performance and increases re-usability of mapped scenes. Furthermore, we add color integration which notably improves color-correctness at adjacent surfaces. Our novel formulation of combined ICP with frame-to-keyframe photometric error minimization further improves tracking results. Lastly, we introduce Sim(3) point-to-plane ICP for refining pose priors in a multi-sensor scenario with different scale factors.}
}

C. O. Gonçalves Bazzo, B. Kamali, C. Hütt, G. Bareth, and T. Gaiser, "A Review of Estimation Methods for Aboveground Biomass in Grasslands Using UAV," Remote Sensing, vol. 15, iss. 3, 2023. doi:10.3390/rs15030639
[BibTeX] [PDF]

Grasslands are one of the world’s largest ecosystems, accounting for 30% of total terrestrial biomass. Considering that aboveground biomass (AGB) is one of the most essential ecosystem services in grasslands, an accurate and faster method for estimating AGB is critical for managing, protecting, and promoting ecosystem sustainability. Unmanned aerial vehicles (UAVs) have emerged as a useful and practical tool for achieving this goal. Here, we review recent research studies that employ UAVs to estimate AGB in grassland ecosystems. We summarize different methods to establish a comprehensive workflow, from data collection in the field to data processing. For this purpose, 64 research articles were reviewed, focusing on several features including study site, grassland species composition, UAV platforms, flight parameters, sensors, field measurement, biomass indices, data processing, and analysis methods. The results demonstrate that there has been an increase in scientific research evaluating the use of UAVs in AGB estimation in grasslands during the period 2018–2022. Most of the studies were carried out in three countries (Germany, China, and USA), which indicates an urgent need for research in other locations where grassland ecosystems are abundant. We found RGB imaging was the most commonly used and is the most suitable for estimating AGB in grasslands at the moment, in terms of cost–benefit and data processing simplicity. In 50% of the studies, at least one vegetation index was used to estimate AGB; the Normalized Difference Vegetation Index (NDVI) was the most common. The most popular methods for data analysis were linear regression, partial least squares regression (PLSR), and random forest. Studies that used spectral and structural data showed that models incorporating both data types outperformed models utilizing only one. We also observed that research in this field has been limited both spatially and temporally. For example, only a small number of papers conducted studies over a number of years and in multiple places, suggesting that the protocols are not transferable to other locations and time points. Despite these limitations, and in the light of the rapid advances, we anticipate that UAV methods for AGB estimation in grasslands will continue improving and may become commercialized for farming applications in the near future.

@Article{rs15030639,
AUTHOR = {Gonçalves Bazzo, Clara Oliva and Kamali, Bahareh and Hütt, Christoph and Bareth, Georg and Gaiser, Thomas},
TITLE = {A Review of Estimation Methods for Aboveground Biomass in Grasslands Using UAV},
JOURNAL = {Remote Sensing},
VOLUME = {15},
YEAR = {2023},
NUMBER = {3},
ARTICLE-NUMBER = {639},
URL = {https://www.mdpi.com/2072-4292/15/3/639},
ISSN = {2072-4292},
ABSTRACT = {Grasslands are one of the world’s largest ecosystems, accounting for 30% of total terrestrial biomass. Considering that aboveground biomass (AGB) is one of the most essential ecosystem services in grasslands, an accurate and faster method for estimating AGB is critical for managing, protecting, and promoting ecosystem sustainability. Unmanned aerial vehicles (UAVs) have emerged as a useful and practical tool for achieving this goal. Here, we review recent research studies that employ UAVs to estimate AGB in grassland ecosystems. We summarize different methods to establish a comprehensive workflow, from data collection in the field to data processing. For this purpose, 64 research articles were reviewed, focusing on several features including study site, grassland species composition, UAV platforms, flight parameters, sensors, field measurement, biomass indices, data processing, and analysis methods. The results demonstrate that there has been an increase in scientific research evaluating the use of UAVs in AGB estimation in grasslands during the period 2018–2022. Most of the studies were carried out in three countries (Germany, China, and USA), which indicates an urgent need for research in other locations where grassland ecosystems are abundant. We found RGB imaging was the most commonly used and is the most suitable for estimating AGB in grasslands at the moment, in terms of cost–benefit and data processing simplicity. In 50% of the studies, at least one vegetation index was used to estimate AGB; the Normalized Difference Vegetation Index (NDVI) was the most common. The most popular methods for data analysis were linear regression, partial least squares regression (PLSR), and random forest. Studies that used spectral and structural data showed that models incorporating both data types outperformed models utilizing only one. We also observed that research in this field has been limited both spatially and temporally. For example, only a small number of papers conducted studies over a number of years and in multiple places, suggesting that the protocols are not transferable to other locations and time points. Despite these limitations, and in the light of the rapid advances, we anticipate that UAV methods for AGB estimation in grasslands will continue improving and may become commercialized for farming applications in the near future.},
DOI = {10.3390/rs15030639}
}

C. Gebauer, N. Dengler, and M. Bennewitz, "Sensor-Based Navigation Using Hierarchical Reinforcement Learning," in Intelligent Autonomous Systems 17 , Cham, 2023, p. 546–560. doi:10.1007/978-3-031-22216-0_37
[BibTeX] [PDF]

Robotic systems are nowadays capable of solving complex navigation tasks. However, their capabilities are limited to the knowledge of the designer and consequently lack generalizability to initially unconsidered situations. This makes deep reinforcement learning (DRL) especially interesting, as these algorithms promise a self-learning system only relying on feedback from the environment. In this paper, we consider the problem of lidar-based robot navigation in continuous action space using DRL without providing any goal-oriented or global information. By relying solely on local sensor data to solve navigation tasks, we design an agent that assigns its own waypoints based on intrinsic motivation. Our agent is able to learn goal-directed navigation behavior even when facing only sparse feedback, i.e., delayed rewards when reaching the target. To address this challenge and the complexity of the continuous action space, we deploy a hierarchical agent structure in which the exploration is distributed across multiple layers. Within the hierarchical structure, our agent self-assigns internal goals and learns to extract reasonable waypoints to reach the desired target position only based on local sensor data. In our experiments, we demonstrate the navigation capabilities of our agent in two environments and show that the hierarchical structure seriously improves the performance in terms of success rate and success weighted by path length in comparison to a flat structure. Furthermore, we provide a real-robot experiment to illustrate that the trained agent can be easily transferred to a real-world scenario.

@InProceedings{10.1007/978-3-031-22216-0_37,
author={Gebauer, Christopher and Dengler, Nils and Bennewitz, Maren},
editor={Petrovic, Ivan and Menegatti, Emanuele and Markovi{\'{c}}, Ivan},
title={Sensor-Based Navigation Using Hierarchical Reinforcement Learning},
booktitle={Intelligent Autonomous Systems 17},
year={2023},
publisher={Springer Nature Switzerland},
address={Cham},
pages={546--560},
url={https://arxiv.org/pdf/2108.13268.pdf},
doi={10.1007/978-3-031-22216-0_37},
abstract={Robotic systems are nowadays capable of solving complex navigation tasks. However, their capabilities are limited to the knowledge of the designer and consequently lack generalizability to initially unconsidered situations. This makes deep reinforcement learning (DRL) especially interesting, as these algorithms promise a self-learning system only relying on feedback from the environment. In this paper, we consider the problem of lidar-based robot navigation in continuous action space using DRL without providing any goal-oriented or global information. By relying solely on local sensor data to solve navigation tasks, we design an agent that assigns its own waypoints based on intrinsic motivation. Our agent is able to learn goal-directed navigation behavior even when facing only sparse feedback, i.e., delayed rewards when reaching the target. To address this challenge and the complexity of the continuous action space, we deploy a hierarchical agent structure in which the exploration is distributed across multiple layers. Within the hierarchical structure, our agent self-assigns internal goals and learns to extract reasonable waypoints to reach the desired target position only based on local sensor data. In our experiments, we demonstrate the navigation capabilities of our agent in two environments and show that the hierarchical structure seriously improves the performance in terms of success rate and success weighted by path length in comparison to a flat structure. Furthermore, we provide a real-robot experiment to illustrate that the trained agent can be easily transferred to a real-world scenario.},
isbn={978-3-031-22216-0}
}

X. Chen, L. Poudel, Z. Hong, P. Johnen, S. Katti, A. Tripathi, A. H. Nile, S. M. Green, D. Khan, G. Schaaf, F. Bono, V. A. Bankaitis, and T. I. Igumenova, "Mechanisms by which small molecules of diverse chemotypes arrest Sec14 lipid transfer activity," Journal of Biological Chemistry, vol. 299, iss. 2, p. 102861, 2023. doi:10.1016/j.jbc.2022.102861
[BibTeX] [PDF]

Phosphatidylinositol (PtdIns) transfer proteins (PITPs) enhance the activities of PtdIns 4-OH kinases that generate signaling pools of PtdIns-4-phosphate. In that capacity, PITPs serve as key regulators of lipid signaling in eukaryotic cells. Although the PITP phospholipid exchange cycle is the engine that stimulates PtdIns 4-OH kinase activities, the underlying mechanism is not understood. Herein, we apply an integrative structural biology approach to investigate interactions of the yeast PITP Sec14 with small-molecule inhibitors (SMIs) of its phospholipid exchange cycle. Using a combination of X-ray crystallography, solution NMR spectroscopy, and atomistic MD simulations, we dissect how SMIs compete with native Sec14 phospholipid ligands and arrest phospholipid exchange. Moreover, as Sec14 PITPs represent new targets for the development of next-generation antifungal drugs, the structures of Sec14 bound to SMIs of diverse chemotypes reported in this study will provide critical information required for future structure-based design of next-generation lead compounds directed against Sec14 PITPs of virulent fungi.

@article{CHEN2023102861,
title = {Mechanisms by which small molecules of diverse chemotypes arrest Sec14 lipid transfer activity},
journal = {Journal of Biological Chemistry},
volume = {299},
number = {2},
pages = {102861},
year = {2023},
issn = {0021-9258},
doi = {10.1016/j.jbc.2022.102861},
url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9898755/},
author = {Xiao-Ru Chen and Lokendra Poudel and Zebin Hong and Philipp Johnen and Sachin Katti and Ashutosh Tripathi and Aaron H. Nile and Savana M. Green and Danish Khan and Gabriel Schaaf and Fulvia Bono and Vytas A. Bankaitis and Tatyana I. Igumenova},
keywords = {Sec14 PITPs, phosphoinositides, protein and lipid dynamics, lipid exchange, anti-mycotic drugs},
abstract = {Phosphatidylinositol (PtdIns) transfer proteins (PITPs) enhance the activities of PtdIns 4-OH kinases that generate signaling pools of PtdIns-4-phosphate. In that capacity, PITPs serve as key regulators of lipid signaling in eukaryotic cells. Although the PITP phospholipid exchange cycle is the engine that stimulates PtdIns 4-OH kinase activities, the underlying mechanism is not understood. Herein, we apply an integrative structural biology approach to investigate interactions of the yeast PITP Sec14 with small-molecule inhibitors (SMIs) of its phospholipid exchange cycle. Using a combination of X-ray crystallography, solution NMR spectroscopy, and atomistic MD simulations, we dissect how SMIs compete with native Sec14 phospholipid ligands and arrest phospholipid exchange. Moreover, as Sec14 PITPs represent new targets for the development of next-generation antifungal drugs, the structures of Sec14 bound to SMIs of diverse chemotypes reported in this study will provide critical information required for future structure-based design of next-generation lead compounds directed against Sec14 PITPs of virulent fungi.}
}

Y. Wu, J. Kuang, X. Niu, J. Behley, L. Klingbeil, and H. Kuhlmann, "Wheel-SLAM: Simultaneous Localization and Terrain Mapping Using One Wheel-Mounted IMU," IEEE Robotics and Automation Letters, vol. 8, iss. 1, pp. 280-287, 2023. doi:10.1109/LRA.2022.3226071
[BibTeX] [PDF] [Code]

@ARTICLE{9968088,
author={Wu, Yibin and Kuang, Jian and Niu, Xiaoji and Behley, Jens and Klingbeil, Lasse and Kuhlmann, Heiner},
journal={IEEE Robotics and Automation Letters},
title={Wheel-SLAM: Simultaneous Localization and Terrain Mapping Using One Wheel-Mounted IMU},
year={2023},
volume={8},
number={1},
pages={280-287},
codeurl={https://github.com/i2Nav-WHU/Wheel-SLAM},
url={https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/wu2023ral.pdf},
doi={10.1109/LRA.2022.3226071}}

S. Kelly, A. Riccardi, E. Marks, F. Magistri, T. Guadagnino, M. Chli, and C. Stachniss, "Target-Aware Implicit Mapping for Agricultural Crop Inspection," in Proc.~of the IEEE Intl.~Conf.~on Robotics & Automation (ICRA) , 2023. doi:10.1109/icra48891.2023.10160487
[BibTeX] [PDF] [Video]

@inproceedings{kelly2023icra,
author = {S. Kelly and A. Riccardi and E. Marks and F. Magistri and T. Guadagnino and M. Chli and C. Stachniss},
url={https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/kelly2023icra.pdf},
doi={10.1109/icra48891.2023.10160487},
title = {{Target-Aware Implicit Mapping for Agricultural Crop Inspection}},
booktitle = {Proc.~of the IEEE Intl.~Conf.~on Robotics \& Automation (ICRA)},
videourl = {https://youtu.be/UAIqn0QnpKg},
year = {2023}}

A. Riccardi, S. Kelly, E. Marks, F. Magistri, T. Guadagnino, J. Behley, M. Bennewitz, and C. Stachniss, "Fruit Tracking Over Time Using High-Precision Point Clouds," in Proc.~of the IEEE Intl.~Conf.~on Robotics & Automation (ICRA) , 2023. doi:10.1109/icra48891.2023.10161350
[BibTeX] [PDF] [Video]

@inproceedings{riccardi2023icra,
author = {A. Riccardi and S. Kelly and E. Marks and F. Magistri and T. Guadagnino and J. Behley and M. Bennewitz and C. Stachniss},
title = {{Fruit Tracking Over Time Using High-Precision Point Clouds}},
booktitle = {Proc.~of the IEEE Intl.~Conf.~on Robotics \& Automation (ICRA)},
year = {2023},
url={https://www.researchgate.net/publication/372118494_Fruit_Tracking_Over_Time_Using_High-Precision_Point_Clouds#full-text},
doi={10.1109/icra48891.2023.10161350},
videourl = {https://youtu.be/fBGSd0--PXY}
}

G. Roggiolani, M. Sodano, F. Magistri, T. Guadagnino, J. Behley, and C. Stachniss, "Hierarchical Approach for Joint Semantic, Plant Instance, and Leaf Instance Segmentation in the Agricultural Domain," in Proc.~of the IEEE Intl.~Conf.~on Robotics & Automation (ICRA) , 2023. doi:10.1109/icra48891.2023.10160918
[BibTeX] [PDF] [Code] [Video]

@inproceedings{roggiolani2023icra-hajs,
author = {G. Roggiolani and M. Sodano and F. Magistri and T. Guadagnino and J. Behley and C. Stachniss},
title = {{Hierarchical Approach for Joint Semantic, Plant Instance, and Leaf Instance Segmentation in the Agricultural Domain}},
booktitle = {Proc.~of the IEEE Intl.~Conf.~on Robotics \& Automation (ICRA)},
year = {2023},
doi={10.1109/icra48891.2023.10160918},
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/roggiolani2023icra-hajs.pdf},
codeurl = {https://github.com/PRBonn/HAPT},
videourl = {https://youtu.be/miuOJjxlJic}
}

G. Roggiolani, F. Magistri, T. Guadagnino, G. Grisetti, C. Stachniss, and J. Behley, "On Domain-Specific Pre-Training for Effective Semantic Perception in Agricultural Robotics," in Proc.~of the IEEE Intl.~Conf.~on Robotics & Automation (ICRA) , 2023. doi:10.1109/icra48891.2023.10160624
[BibTeX] [PDF] [Code] [Video]

@inproceedings{roggiolani2023icra-odsp,
author = {G. Roggiolani and F. Magistri and T. Guadagnino and G. Grisetti and C. Stachniss and J. Behley},
title = {{On Domain-Specific Pre-Training for Effective Semantic Perception in Agricultural Robotics}},
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/roggiolani2023icra-odsp.pdf},
codeurl = {https://github.com/PRBonn/agri-pretraining},
doi={10.1109/icra48891.2023.10160624},
booktitle = {Proc.~of the IEEE Intl.~Conf.~on Robotics \& Automation (ICRA)},
year = {2023},
videourl = {https://youtu.be/FDWY_UnfsBs}
}

H. Unnikrishnan, M. K. Gerullis, M. Cox, and H. Nagendra, "Unpacking dynamics of diverse nested resource systems through a diagnostic approach," Sustainability Science, p. 1–28, 2023. doi:10.1007/s11625-022-01268-y
[BibTeX] [PDF]

The social–ecological systems (SES) framework (Ostrom 2009, Science. 325(5939):419–22) typologically decomposes SES characteristics into nested, tiered constituent variables. Yet, aligning the framework’s concepts of resource system (RS) and resource unit (RU) with realities of individual case studies poses challenges if the underlying SES is not a single RS, but a mid to large-scale nested RS (NRS). Using a diagnostic approach, we describe NRSs—and the activities and networks of adjacent action situations (NAAS) containing them. An NRS includes the larger RS and multiple interlinked semi-autonomous subsidiary RSs, each of which support simultaneous, differently managed appropriation of individual RUs. We further identify NAASs operating within NRSs in two diverse empirical cases—networks of lake systems in Bengaluru, India and German wheat breeding systems—representing a lever towards understanding transformation of SESs into sustainable futures. This paper contributes towards unpacking and diagnosing complexities within mid to large-scale RSs and their governance. It provides a generalizable, rigorous approach to SES case study analyses, thereby advancing methods for synthesis in sustainability science.

@article{gerullissustainability,
author = {Unnikrishnan, Hita and Gerullis, Maria Katharina and Cox, Michael and Nagendra, Harini},
title = {Unpacking dynamics of diverse nested resource systems through a diagnostic approach},
journal = {Sustainability Science},
year = {2023},
pages={1--28},
abstract = {The social–ecological systems (SES) framework (Ostrom 2009, Science. 325(5939):419–22) typologically decomposes SES characteristics into nested, tiered constituent variables. Yet, aligning the framework’s concepts of resource system (RS) and resource unit (RU) with realities of individual case studies poses challenges if the underlying SES is not a single RS, but a mid to large-scale nested RS (NRS). Using a diagnostic approach, we describe NRSs—and the activities and networks of adjacent action situations (NAAS) containing them. An NRS includes the larger RS and multiple interlinked semi-autonomous subsidiary RSs, each of which support simultaneous, differently managed appropriation of individual RUs. We further identify NAASs operating within NRSs in two diverse empirical cases—networks of lake systems in Bengaluru, India and German wheat breeding systems—representing a lever towards understanding transformation of SESs into sustainable futures. This paper contributes towards unpacking and diagnosing complexities within mid to large-scale RSs and their governance. It provides a generalizable, rigorous approach to SES case study analyses, thereby advancing methods for synthesis in sustainability science.},
url = {https://doi.org/10.1007/s11625-022-01268-y},
doi = {10.1007/s11625-022-01268-y}}

G. Lopez, S. H. Ahmadi, W. Amelung, M. Athmann, F. Ewert, T. Gaiser, M. I. Gocke, T. Kautz, J. Postma, S. Rachmilevitch, G. Schaaf, A. Schnepf, A. Stoschus, M. Watt, P. Yu, and S. J. Seidel, "Nutrient deficiency effects on root architecture and root-to-shoot ratio in arable crops," Frontiers in Plant Science, vol. 13, 2023. doi:10.3389/fpls.2022.1067498
[BibTeX] [PDF]

Plant root traits play a crucial role in resource acquisition and crop performance when soil nutrient availability is low. However, the respective trait responses are complex, particularly at the field scale, and poorly understood due to difficulties in root phenotyping monitoring, inaccurate sampling, and environmental conditions. Here, we conducted a systematic review and meta-analysis of 50 field studies to identify the effects of nitrogen (N), phosphorous (P), or potassium (K) deficiencies on the root systems of common crops. Root length and biomass were generally reduced, while root length per shoot biomass was enhanced under N and P deficiency. Root length decreased by 9% under N deficiency and by 14% under P deficiency, while root biomass was reduced by 7% in N-deficient and by 25% in P-deficient soils. Root length per shoot biomass increased by 33% in N deficient and 51% in P deficient soils. The root-to-shoot ratio was often enhanced (44%) under N-poor conditions, but no consistent response of the root-to-shoot ratio to P-deficiency was found. Only a few K-deficiency studies suited our approach and, in those cases, no differences in morphological traits were reported. We encountered the following drawbacks when performing this analysis: limited number of root traits investigated at field scale, differences in the timing and severity of nutrient deficiencies, missing data (e.g., soil nutrient status and time of stress), and the impact of other conditions in the field. Nevertheless, our analysis indicates that, in general, nutrient deficiencies increased the root-length-to-shoot-biomass ratios of crops, with impacts decreasing in the order deficient P > deficient N > deficient K. Our review resolved inconsistencies that were often found in the individual field experiments, and led to a better understanding of the physiological mechanisms underlying root plasticity in fields with low nutrient availability.

@ARTICLE{10.3389/fpls.2022.1067498,
AUTHOR={Lopez, Gina and Ahmadi, Seyed Hamid and Amelung, Wulf and Athmann, Miriam and Ewert, Frank and Gaiser, Thomas and Gocke, Martina I. and Kautz, Timo and Postma, Johannes and Rachmilevitch, Shimon and Schaaf, Gabriel and Schnepf, Andrea and Stoschus, Alixandrine and Watt, Michelle and Yu, Peng and Seidel, Sabine Julia},
TITLE={Nutrient deficiency effects on root architecture and root-to-shoot ratio in arable crops},
JOURNAL={Frontiers in Plant Science},
VOLUME={13},
YEAR={2023},
URL={https://www.frontiersin.org/articles/10.3389/fpls.2022.1067498},
DOI={10.3389/fpls.2022.1067498},
ISSN={1664-462X},
ABSTRACT={Plant root traits play a crucial role in resource acquisition and crop performance when soil nutrient availability is low. However, the respective trait responses are complex, particularly at the field scale, and poorly understood due to difficulties in root phenotyping monitoring, inaccurate sampling, and environmental conditions. Here, we conducted a systematic review and meta-analysis of 50 field studies to identify the effects of nitrogen (N), phosphorous (P), or potassium (K) deficiencies on the root systems of common crops. Root length and biomass were generally reduced, while root length per shoot biomass was enhanced under N and P deficiency. Root length decreased by 9% under N deficiency and by 14% under P deficiency, while root biomass was reduced by 7% in N-deficient and by 25% in P-deficient soils. Root length per shoot biomass increased by 33% in N deficient and 51% in P deficient soils. The root-to-shoot ratio was often enhanced (44%) under N-poor conditions, but no consistent response of the root-to-shoot ratio to P-deficiency was found. Only a few K-deficiency studies suited our approach and, in those cases, no differences in morphological traits were reported. We encountered the following drawbacks when performing this analysis: limited number of root traits investigated at field scale, differences in the timing and severity of nutrient deficiencies, missing data (e.g., soil nutrient status and time of stress), and the impact of other conditions in the field. Nevertheless, our analysis indicates that, in general, nutrient deficiencies increased the root-length-to-shoot-biomass ratios of crops, with impacts decreasing in the order deficient P > deficient N > deficient K. Our review resolved inconsistencies that were often found in the individual field experiments, and led to a better understanding of the physiological mechanisms underlying root plasticity in fields with low nutrient availability.}
}

H. Dong, X. Chen, S. Särkkä, and C. Stachniss, "Online pole segmentation on range images for long-term LiDAR localization in urban environments," Robotics and Autonomous Systems, vol. 159, p. 104283, 2023. doi:10.1016/j.robot.2022.104283
[BibTeX] [PDF] [Code]

Robust and accurate localization is a basic requirement for mobile autonomous systems. Pole-like objects, such as traffic signs, poles, and lamps are frequently used landmarks for localization in urban environments due to their local distinctiveness and long-term stability. In this paper, we present a novel, accurate, and fast pole extraction approach based on geometric features that runs online and has little computational demands. Our method performs all computations directly on range images generated from 3D LiDAR scans, which avoids processing 3D point clouds explicitly and enables fast pole extraction for each scan. We further use the extracted poles as pseudo labels to train a deep neural network for online range image-based pole segmentation. We test both our geometric and learning-based pole extraction methods for localization on different datasets with different LiDAR scanners, routes, and seasonal changes. The experimental results show that our methods outperform other state-of-the-art approaches. Moreover, boosted with pseudo pole labels extracted from multiple datasets, our learning-based method can run across different datasets and achieve even better localization results compared to our geometry-based method. We released our pole datasets to the public for evaluating the performance of pole extractors, as well as the implementation of our approach.

@article{DONG2023104283,
title = {Online pole segmentation on range images for long-term LiDAR localization in urban environments},
journal = {Robotics and Autonomous Systems},
volume = {159},
pages = {104283},
year = {2023},
issn = {0921-8890},
doi = {10.1016/j.robot.2022.104283},
url = {https://arxiv.org/pdf/2208.07364},
codeurl = {https://github.com/PRBonn/pole-localization},
author = {Hao Dong and Xieyuanli Chen and Simo Särkkä and Cyrill Stachniss},
keywords = {Localization, Pole, LiDAR, Range image, Mapping, Autonomous driving, Deep learning, Semantic segmentation},
abstract = {Robust and accurate localization is a basic requirement for mobile autonomous systems. Pole-like objects, such as traffic signs, poles, and lamps are frequently used landmarks for localization in urban environments due to their local distinctiveness and long-term stability. In this paper, we present a novel, accurate, and fast pole extraction approach based on geometric features that runs online and has little computational demands. Our method performs all computations directly on range images generated from 3D LiDAR scans, which avoids processing 3D point clouds explicitly and enables fast pole extraction for each scan. We further use the extracted poles as pseudo labels to train a deep neural network for online range image-based pole segmentation. We test both our geometric and learning-based pole extraction methods for localization on different datasets with different LiDAR scanners, routes, and seasonal changes. The experimental results show that our methods outperform other state-of-the-art approaches. Moreover, boosted with pseudo pole labels extracted from multiple datasets, our learning-based method can run across different datasets and achieve even better localization results compared to our geometry-based method. We released our pole datasets to the public for evaluating the performance of pole extractors, as well as the implementation of our approach.}}

F. Stache, J. Westheider, F. Magistri, C. Stachniss, and M. Popović, "Adaptive path planning for UAVs for multi-resolution semantic segmentation," Robotics and Autonomous Systems, vol. 159, p. 104288, 2023. doi:10.1016/j.robot.2022.104288
[BibTeX]

Efficient data collection methods play a major role in helping us better understand the Earth and its ecosystems. In many applications, the usage of unmanned aerial vehicles (UAVs) for monitoring and remote sensing is rapidly gaining momentum due to their high mobility, low cost, and flexible deployment. A key challenge is planning missions to maximize the value of acquired data in large environments given flight time limitations. This is, for example, relevant for monitoring agricultural fields. This paper addresses the problem of adaptive path planning for accurate semantic segmentation of using UAVs. We propose an online planning algorithm which adapts the UAV paths to obtain high-resolution semantic segmentations necessary in areas with fine details as they are detected in incoming images. This enables us to perform close inspections at low altitudes only where required, without wasting energy on exhaustive mapping at maximum image resolution. A key feature of our approach is a new accuracy model for deep learning-based architectures that captures the relationship between UAV altitude and semantic segmentation accuracy. We evaluate our approach on different domains using real-world data, proving the efficacy and generability of our solution.

@article{STACHE2023104288,
title = {Adaptive path planning for UAVs for multi-resolution semantic segmentation},
journal = {Robotics and Autonomous Systems},
volume = {159},
pages = {104288},
year = {2023},
issn = {0921-8890},
doi = {10.1016/j.robot.2022.104288},
author = {Felix Stache and Jonas Westheider and Federico Magistri and Cyrill Stachniss and Marija Popovi{\'c}},
keywords = {Unmanned aerial vehicles, Semantic segmentation, Planning, Terrain monitoring}, abstract = {Efficient data collection methods play a major role in helping us better understand the Earth and its ecosystems. In many applications, the usage of unmanned aerial vehicles (UAVs) for monitoring and remote sensing is rapidly gaining momentum due to their high mobility, low cost, and flexible deployment. A key challenge is planning missions to maximize the value of acquired data in large environments given flight time limitations. This is, for example, relevant for monitoring agricultural fields. This paper addresses the problem of adaptive path planning for accurate semantic segmentation of using UAVs. We propose an online planning algorithm which adapts the UAV paths to obtain high-resolution semantic segmentations necessary in areas with fine details as they are detected in incoming images. This enables us to perform close inspections at low altitudes only where required, without wasting energy on exhaustive mapping at maximum image resolution. A key feature of our approach is a new accuracy model for deep learning-based architectures that captures the relationship between UAV altitude and semantic segmentation accuracy. We evaluate our approach on different domains using real-world data, proving the efficacy and generability of our solution.}}

M. Arora, L. Wiesmann, X. Chen, and C. Stachniss, "Static map generation from 3D LiDAR point clouds exploiting ground segmentation," Robotics and Autonomous Systems, vol. 159, pp. 104-287, 2023. doi:10.1016/j.robot.2022.104287
[BibTeX] [PDF] [Code]

@article{arora2023static,
title={Static map generation from 3D LiDAR point clouds exploiting ground segmentation},
author={Arora, Mehul and Wiesmann, Louis and Chen, Xieyuanli and Stachniss, Cyrill},
journal={Robotics and Autonomous Systems},
volume={159},
pages={104-287},
issn = {0921-8890},
year={2023},
doi={10.1016/j.robot.2022.104287},
codeurl={https://github.com/PRBonn/dynamic-point-removal},
url={https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/arora2023jras.pdf}}

L. Wiesmann, L. Nunes, J. Behley, and C. Stachniss, "KPPR: Exploiting Momentum Contrast for Point Cloud-Based Place Recognition," IEEE Robotics and Automation Letters, vol. 8, iss. 2, pp. 592-599, 2023. doi:10.1109/LRA.2022.3228174
[BibTeX] [PDF] [Code] [Video]

@article{wiesmann2023ral,
author = {L. Wiesmann and L. Nunes and J. Behley and C. Stachniss},
title = {{KPPR: Exploiting Momentum Contrast for Point Cloud-Based Place Recognition}},
journal = {IEEE Robotics and Automation Letters},
url={https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/wiesmann2023ral.pdf},
volume = {8},
number = {2},
pages = {592-599},
year = 2023,
issn = {2377-3766},
doi = {10.1109/LRA.2022.3228174},
codeurl = {https://github.com/PRBonn/kppr},
videourl = {https://youtu.be/bICz1sqd8Xs}
}

M. R. Paul, D. T. Demie, S. J. Seidel, and T. F. Döring, "Effects of spring wheat / faba bean mixtures on early crop development," Plant and Soil, 2023. doi:10.1007/s11104-023-06111-6
[BibTeX] [PDF] [Video]

Intercropping cereals and grain legumes has the potential to increase grain yield in comparison to the respective sole crops, but little is known about mixture effects at the early crop developmental stage. In cereal legume mixtures, the cereal is usually the dominating partner. We aimed to find out when domination starts, which factors may enhance early domination, and if there is a legacy effect of early domination on later growth stages.

@article{paul2023effects,
title={Effects of spring wheat / faba bean mixtures on early crop development},
author={Paul, Madhuri Rani and Demie, Dereje T and Seidel, Sabine J and Döring, Thomas Felix},
year={2023},
doi={10.1007/s11104-023-06111-6},
abstract={Intercropping cereals and grain legumes has the potential to increase grain yield in comparison to the respective sole crops, but little is known about mixture effects at the early crop developmental stage. In cereal legume mixtures, the cereal is usually the dominating partner. We aimed to find out when domination starts, which factors may enhance early domination, and if there is a legacy effect of early domination on later growth stages.},
journal={Plant and Soil},
videourl={https://www.youtube.com/watch?v=Jtn9udsNWIA},
url={https://doi.org/10.1007/s11104-023-06111-6}}

R. Hossain, F. R. Ispizua Yamati, A. Barreto, F. Savian, M. Varrelmann, A. Mahlein, and S. Paulus, "Elucidation of turnip yellows virus (TuYV) spectral reflectance pattern in Nicotiana benthamiana by non-imaging sensor technology," Journal of Plant Diseases and Protection, 2023. doi:10.1007/s41348-022-00682-9
[BibTeX]

@article{Hossain_Plantdiseases,
title = {Elucidation of turnip yellows virus (TuYV) spectral reflectance pattern in Nicotiana benthamiana by non-imaging sensor technology},
journal = {Journal of Plant Diseases and Protection},
year = {2023},
doi = {10.1007/s41348-022-00682-9},
author = {Hossain, Roxana and Ispizua Yamati, Facundo Ramón and Barreto, Abel and Savian, Francesco and Varrelmann, Mark and Mahlein, Anne-Kathrin and Paulus, Stefan},
}

A. Brugger, I. F. R. Yamati, A. Barreto, S. Paulus, P. Schramowski, K. Kersting, U. Steiner, S. Neugart, and A. -K. Mahlein, "Hyperspectral imaging in the UV range allows for differentiation of sugar beet diseases based on changes of secondary plant metabolites," Phytopathology, 2023. doi:10.1094/PHYTO-03-22-0086-R
[BibTeX] [PDF]

@article{Brugger_Pythopathology,
title = {Hyperspectral imaging in the UV range allows for differentiation of sugar beet diseases based on changes of secondary plant metabolites},
journal = {Phytopathology},
year = {2023},
doi = {10.1094/PHYTO-03-22-0086-R},
URL = {https://apsjournals.apsnet.org/doi/10.1094/PHYTO-03-22-0086-R?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed},
author = {A. Brugger and F. R. Ispizua Yamati and A. Barreto and S. Paulus and P. Schramowski and K. Kersting and U. Steiner and S. Neugart and A.-K. Mahlein}
}

2022

M. Donat, J. Geistert, K. Grahmann, R. Bloch, and S. D. Bellingrath-Kimura, "Patch cropping- a new methodological approach to determine new field arrangements that increase the multifunctionality of agricultural landscapes," Computers and Electronics in Agriculture, vol. 197, p. 106894, 2022. doi:10.1016/j.compag.2022.106894
[BibTeX] [PDF]

Agricultural intensification decreased land cover complexity by converting small complex arable field geometries into large and simple structures which then were managed uniformly. These changes have led to a variety of negative environmental effects and influence ecosystem services. We present a novel small-scale and site-specific cropping system which splits a large field into small homogeneous sub-fields called ‘patches’ grouped in different yield potentials. A detailed workflow is presented to generate new spatially arranged patches with special focus on preprocessing and filtering of multi-year yield data, the variation in patch sizes and the adaptation of maximum working width to use available conventional farm equipment and permanent traffic lanes. The reduction of variance by the used cluster algorithm depends on the within-field heterogeneity. The patch size, the number of growing seasons (GS) used for clustering and the parallel shift of the patch structure along the permanent traffic lane resulted in a change in relative variance. Independent cross validation showed an increased performance of the classification algorithm with increasing number of GS used for clustering. The applied cluster analysis resulted in robust field segregation according to different yield potential zones and provides an innovative method for a novel cropping system.

@article{DONAT2022106894,
title = {Patch cropping- a new methodological approach to determine new field arrangements that increase the multifunctionality of agricultural landscapes},
journal = {Computers and Electronics in Agriculture},
volume = {197},
pages = {106894},
year = {2022},
issn = {0168-1699},
doi = {10.1016/j.compag.2022.106894},
url = {https://www.sciencedirect.com/science/article/pii/S0168169922002113},
author = {Marco Donat and Jonas Geistert and Kathrin Grahmann and Ralf Bloch and Sonoko D. Bellingrath-Kimura},
keywords = {Soil Management Zone Delineation, Yield Productivity Zones, Python, Clustering},
abstract = {Agricultural intensification decreased land cover complexity by converting small complex arable field geometries into large and simple structures which then were managed uniformly. These changes have led to a variety of negative environmental effects and influence ecosystem services. We present a novel small-scale and site-specific cropping system which splits a large field into small homogeneous sub-fields called ‘patches’ grouped in different yield potentials. A detailed workflow is presented to generate new spatially arranged patches with special focus on preprocessing and filtering of multi-year yield data, the variation in patch sizes and the adaptation of maximum working width to use available conventional farm equipment and permanent traffic lanes. The reduction of variance by the used cluster algorithm depends on the within-field heterogeneity. The patch size, the number of growing seasons (GS) used for clustering and the parallel shift of the patch structure along the permanent traffic lane resulted in a change in relative variance. Independent cross validation showed an increased performance of the classification algorithm with increasing number of GS used for clustering. The applied cluster analysis resulted in robust field segregation according to different yield potential zones and provides an innovative method for a novel cropping system.}
}

S. Marangoz, T. Zaenker, R. Menon, and M. Bennewitz, "Fruit Mapping with Shape Completion for Autonomous Crop Monitoring," in 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) , 2022, pp. 471-476. doi:10.1109/CASE49997.2022.9926466
[BibTeX] [PDF]

@INPROCEEDINGS{9926466,
author={Marangoz, Salih and Zaenker, Tobias and Menon, Rohit and Bennewitz, Maren},
booktitle={2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)},
title={Fruit Mapping with Shape Completion for Autonomous Crop Monitoring},
year={2022},
volume={},
number={},
pages={471-476},
doi={10.1109/CASE49997.2022.9926466},
URL= {https://arxiv.org/pdf/2203.15489},
}

N. P. Laha, R. F. H. Giehl, E. Riemer, D. Qiu, N. J. Pullagurla, R. Schneider, Y. W. Dhir, R. Yadav, Y. E. Mihiret, P. Gaugler, V. Gaugler, H. Mao, N. Zheng, N. von Wirén, A. Saiardi, S. Bhattacharjee, H. J. Jessen, D. Laha, and G. Schaaf, "INOSITOL (1,3,4) TRIPHOSPHATE 5/6 KINASE1-dependent inositol polyphosphates regulate auxin responses in Arabidopsis," Plant Physiology, vol. 190, iss. 4, pp. 2722-2738, 2022. doi:10.1093/plphys/kiac425
[BibTeX] [PDF]

{The combinatorial phosphorylation of myo-inositol results in the generation of different inositol phosphates (InsPs), of which phytic acid (InsP6) is the most abundant species in eukaryotes. InsP6 is also an important precursor of the higher phosphorylated inositol pyrophosphates (PP-InsPs), such as InsP7 and InsP8, which are characterized by a diphosphate moiety and are also ubiquitously found in eukaryotic cells. While PP-InsPs regulate various cellular processes in animals and yeast, their biosynthesis and functions in plants has remained largely elusive because plant genomes do not encode canonical InsP6 kinases. Recent work has shown that Arabidopsis (Arabidopsis thaliana) INOSITOL (1,3,4) TRIPHOSPHATE 5/6 KINASE1 (ITPK1) and ITPK2 display in vitro InsP6 kinase activity and that, in planta, ITPK1 stimulates 5-InsP7 and InsP8 synthesis and regulates phosphate starvation responses. Here we report a critical role of ITPK1 in auxin-related processes that is independent of the ITPK1-controlled regulation of phosphate starvation responses. Those processes include primary root elongation, root hair development, leaf venation, thermomorphogenic and gravitropic responses, and sensitivity to exogenously applied auxin. We found that the recombinant auxin receptor complex, consisting of the F-Box protein TRANSPORT INHIBITOR RESPONSE1 (TIR1), ARABIDOPSIS SKP1 HOMOLOG 1 (ASK1), and the transcriptional repressor INDOLE-3-ACETIC ACID INDUCIBLE 7 (IAA7), binds to anionic inositol polyphosphates with high affinity. We further identified a physical interaction between ITPK1 and TIR1, suggesting a localized production of 5-InsP7, or another ITPK1-dependent InsP/PP-InsP isomer, to activate the auxin receptor complex. Finally, we demonstrate that ITPK1 and ITPK2 function redundantly to control auxin responses, as deduced from the auxin-insensitive phenotypes of itpk1 itpk2 double mutant plants. Our findings expand the mechanistic understanding of auxin perception and suggest that distinct inositol polyphosphates generated near auxin receptors help to fine-tune auxin sensitivity in plants.}

@article{10.1093/plphys/kiac425,
author = {Laha, Nargis Parvin and Giehl, Ricardo F H and Riemer, Esther and Qiu, Danye and Pullagurla, Naga Jyothi and Schneider, Robin and Dhir, Yashika Walia and Yadav, Ranjana and Mihiret, Yeshambel Emewodih and Gaugler, Philipp and Gaugler, Verena and Mao, Haibin and Zheng, Ning and von Wirén, Nicolaus and Saiardi, Adolfo and Bhattacharjee, Saikat and Jessen, Henning J and Laha, Debabrata and Schaaf, Gabriel},
title = "{INOSITOL (1,3,4) TRIPHOSPHATE 5/6 KINASE1-dependent inositol polyphosphates regulate auxin responses in Arabidopsis}",
journal = {Plant Physiology},
volume = {190},
number = {4},
pages = {2722-2738},
year = {2022},
month = {09},
abstract = "{The combinatorial phosphorylation of myo-inositol results in the generation of different inositol phosphates (InsPs), of which phytic acid (InsP6) is the most abundant species in eukaryotes. InsP6 is also an important precursor of the higher phosphorylated inositol pyrophosphates (PP-InsPs), such as InsP7 and InsP8, which are characterized by a diphosphate moiety and are also ubiquitously found in eukaryotic cells. While PP-InsPs regulate various cellular processes in animals and yeast, their biosynthesis and functions in plants has remained largely elusive because plant genomes do not encode canonical InsP6 kinases. Recent work has shown that Arabidopsis (Arabidopsis thaliana) INOSITOL (1,3,4) TRIPHOSPHATE 5/6 KINASE1 (ITPK1) and ITPK2 display in vitro InsP6 kinase activity and that, in planta, ITPK1 stimulates 5-InsP7 and InsP8 synthesis and regulates phosphate starvation responses. Here we report a critical role of ITPK1 in auxin-related processes that is independent of the ITPK1-controlled regulation of phosphate starvation responses. Those processes include primary root elongation, root hair development, leaf venation, thermomorphogenic and gravitropic responses, and sensitivity to exogenously applied auxin. We found that the recombinant auxin receptor complex, consisting of the F-Box protein TRANSPORT INHIBITOR RESPONSE1 (TIR1), ARABIDOPSIS SKP1 HOMOLOG 1 (ASK1), and the transcriptional repressor INDOLE-3-ACETIC ACID INDUCIBLE 7 (IAA7), binds to anionic inositol polyphosphates with high affinity. We further identified a physical interaction between ITPK1 and TIR1, suggesting a localized production of 5-InsP7, or another ITPK1-dependent InsP/PP-InsP isomer, to activate the auxin receptor complex. Finally, we demonstrate that ITPK1 and ITPK2 function redundantly to control auxin responses, as deduced from the auxin-insensitive phenotypes of itpk1 itpk2 double mutant plants. Our findings expand the mechanistic understanding of auxin perception and suggest that distinct inositol polyphosphates generated near auxin receptors help to fine-tune auxin sensitivity in plants.}",
issn = {0032-0889},
doi = {10.1093/plphys/kiac425},
url = {https://doi.org/10.1093/plphys/kiac425},
eprint = {https://academic.oup.com/plphys/article-pdf/190/4/2722/47382607/kiac425.pdf},
}

M. P. T. Jr, T. Heckelei, and S. Rasch, "Aspirations and Investments in Livestock: Evidence of Aspiration Failure in Kenya," in 2022 Agricultural & Applied Economics Association Annual Meeting, Anaheim, CA , 2022. doi:10.22004/ag.econ.322435
[BibTeX]

@inproceedings{raschfailure,
author = {Martin Paul Tabe-Ojong Jr and Thomas Heckelei and Sebastian Rasch},
title = {Aspirations and Investments in Livestock: Evidence of Aspiration Failure in Kenya},
booktitle = {2022 Agricultural & Applied Economics Association Annual Meeting, Anaheim, CA},
year = {2022},
doi = {10.22004/ag.econ.322435 }}

S. J. Seidel, T. Gaiser, A. K. Srivastava, D. Leitner, O. Schmittmann, M. Athmann, T. Kautz, J. Guigue, F. Ewert, and A. Schnepf, "Simulating Root Growth as a Function of Soil Strength and Yield With a Field-Scale Crop Model Coupled With a 3D Architectural Root Model," Frontiers in Plant Science, vol. 13, 2022. doi:doi.org/10.3389/fpls.2022.865188
[BibTeX] [PDF]

@article{seidelfrontiers,
title = {Simulating Root Growth as a Function of Soil Strength and Yield With a Field-Scale Crop Model Coupled With a 3D Architectural Root Model},
journal = {Frontiers in Plant Science},
author = {Sabine Julia Seidel and Thomas Gaiser and Amit Kumar Srivastava and Daniel Leitner and Oliver Schmittmann and Miriam Athmann and Timo Kautz and Julien Guigue and Frank Ewert and Andrea Schnepf},
volume = {13},
year = {2022},
doi = {doi.org/10.3389/fpls.2022.865188},
url = {https://www.frontiersin.org/articles/10.3389/fpls.2022.865188/full?&utm_source=Email_to_authors_&utm_medium=Email&utm_content=T1_11.5e1_author&utm_campaign=Email_publication&field=&journalName=Frontiers_in_Plant_Science&id=865188}
}

K. Abdalla, Y. Sun, M. Zarebanadkouki, T. Gaiser, S. Seidel, and J. Pausch, "Long-term continuous farmyard manure application increases soil carbon when combined with mineral fertilizers due to lower priming effects," Geoderma, vol. 428, p. 116216, 2022. doi:10.1016/j.geoderma.2022.116216
[BibTeX] [PDF]

Organic and synthetic fertilizers not only increase soil fertility and crop productivity but also enhance soil organic carbon (SOC). However, the priming effect (PE) leads to increased soil carbon (C) loss through native SOC mineralization. To date, the mechanisms by which long-term (>66 years) synthetic and/or organic fertilization alters net SOC sequestration remain a matter of debate. This study aimed to assess the effects of different fertilization practices on SOC decomposition and PE in agricultural systems subjected to long-term annual synthetic and/or organic fertilizer application. This aim was achieved by collecting topsoil samples (0–20 cm) from four long-term fertilization practices, i.e., unfertilized, synthetic supplemental (+s), cattle farmyard manure (+m, similar nutrient amount to +s), and synthetic fertilizer with farmyard manure (+s +m, the highest nutrient amount). The soil samples were incubated for 33 days with and without 13C-glucose addition, and a CO2 isotope analyzer combined with a modeling approach was used to establish a real-time method to monitor CO2 and 13CO2 production rates during the incubation period. Overall, +m increased the cumulative SOC-derived CO2 (SOC-CO2) by 107, 74, and 24 % compared to the unfertilized, +s and +s +m, respectively. The higher SOC-CO2 in +m treatment was associated with the greatest priming effect (PE, 390 ± 21 mg C kg soil−1), which corresponded to a 30 % increase compared to the average of the treatments that involved synthetic fertilizer (+s and +s +m) and a 137 % increase compared to the unfertilized control. The results were explained by the lower dissolved nitrogen (N), a proxy of available mineral N, in +m compared to +s +m, thus enhancing microbial mining for additional N via increasing SOC mineralization. However, the combined application of synthetic fertilizer and manure in the +s +m treatment provided enough easily accessible nutrients for microbial growth and activities from the applied synthetic fertilizer, leading to lower SOC mineralization than manure (+m) alone. Nevertheless, the treatments with manure application (i.e., +m and +s +m) significantly increased net SOC compared to the synthetically fertilized treatment and unfertilized control, suggesting greater C inputs than outputs and leading to high SOC accumulation over time. These results indicated that organic manure has a great potential to mitigate climate change by increasing SOC over time, which can be fostered by the addition of synthetic fertilizer; however, caution still needs to be taken regarding the quality and quantity of the added fertilizer.

@article{ABDALLA2022116216,
title = {Long-term continuous farmyard manure application increases soil carbon when combined with mineral fertilizers due to lower priming effects},
journal = {Geoderma},
volume = {428},
pages = {116216},
year = {2022},
issn = {0016-7061},
doi = {10.1016/j.geoderma.2022.116216},
url = {https://www.researchgate.net/publication/364647890_Long-term_continuous_farmyard_manure_application_increases_soil_carbon_when_combined_with_mineral_fertilizers_due_to_lower_priming_effects
},
author = {Khatab Abdalla and Yue Sun and Mohsen Zarebanadkouki and Thomas Gaiser and Sabine Seidel and Johanna Pausch},
keywords = {Basal respiration, Priming effect, Climate change, Fertilization, Soil organic matter},
abstract = {Organic and synthetic fertilizers not only increase soil fertility and crop productivity but also enhance soil organic carbon (SOC). However, the priming effect (PE) leads to increased soil carbon (C) loss through native SOC mineralization. To date, the mechanisms by which long-term (>66 years) synthetic and/or organic fertilization alters net SOC sequestration remain a matter of debate. This study aimed to assess the effects of different fertilization practices on SOC decomposition and PE in agricultural systems subjected to long-term annual synthetic and/or organic fertilizer application. This aim was achieved by collecting topsoil samples (0–20 cm) from four long-term fertilization practices, i.e., unfertilized, synthetic supplemental (+s), cattle farmyard manure (+m, similar nutrient amount to +s), and synthetic fertilizer with farmyard manure (+s +m, the highest nutrient amount). The soil samples were incubated for 33 days with and without 13C-glucose addition, and a CO2 isotope analyzer combined with a modeling approach was used to establish a real-time method to monitor CO2 and 13CO2 production rates during the incubation period. Overall, +m increased the cumulative SOC-derived CO2 (SOC-CO2) by 107, 74, and 24 % compared to the unfertilized, +s and +s +m, respectively. The higher SOC-CO2 in +m treatment was associated with the greatest priming effect (PE, 390 ± 21 mg C kg soil−1), which corresponded to a 30 % increase compared to the average of the treatments that involved synthetic fertilizer (+s and +s +m) and a 137 % increase compared to the unfertilized control. The results were explained by the lower dissolved nitrogen (N), a proxy of available mineral N, in +m compared to +s +m, thus enhancing microbial mining for additional N via increasing SOC mineralization. However, the combined application of synthetic fertilizer and manure in the +s +m treatment provided enough easily accessible nutrients for microbial growth and activities from the applied synthetic fertilizer, leading to lower SOC mineralization than manure (+m) alone. Nevertheless, the treatments with manure application (i.e., +m and +s +m) significantly increased net SOC compared to the synthetically fertilized treatment and unfertilized control, suggesting greater C inputs than outputs and leading to high SOC accumulation over time. These results indicated that organic manure has a great potential to mitigate climate change by increasing SOC over time, which can be fostered by the addition of synthetic fertilizer; however, caution still needs to be taken regarding the quality and quantity of the added fertilizer.}
}

S. Li, M. Cheng, and J. Gall, "Dual Pyramid Generative Adversarial Networks for Semantic Image Synthesis," in 33rd British Machine Vision Conference 2022, BMVC 2022, London, UK , 2022, p. 285. doi:10.48550/arXiv.2210.04085
[BibTeX] [PDF] [Code]

@inproceedings{DBLP:conf/bmvc/LiCG22,
author = {Shijie Li and Ming Cheng and Juergen Gall},
title = {Dual Pyramid Generative Adversarial Networks for Semantic Image Synthesis},
booktitle = {33rd British Machine Vision Conference 2022, {BMVC} 2022, London, UK},
pages = {285},
publisher = {{BMVA} Press},
year = {2022},
doi={10.48550/arXiv.2210.04085},
codeurl = {https://github.com/sj-li/DP_GAN},
url = {https://bmvc2022.mpi-inf.mpg.de/285/},
timestamp = {Thu, 16 Feb 2023 16:15:43 +0100},
biburl = {https://dblp.org/rec/conf/bmvc/LiCG22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}

J. Rückin, L. Jin, F. Magistri, C. Stachniss, and M. Popović, "Informative Path Planning for Active Learning in Aerial Semantic Mapping," in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2022, pp. 11932-11939. doi:10.1109/IROS47612.2022.9981738
[BibTeX] [PDF] [Code]

@INPROCEEDINGS{9981738,
author={Rückin, Julius and Jin, Liren and Magistri, Federico and Stachniss, Cyrill and Popović, Marija},
booktitle={2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
title={Informative Path Planning for Active Learning in Aerial Semantic Mapping},
year={2022},
volume={},
number={},
pages={11932-11939},
url={https://arxiv.org/pdf/2203.01652.pdf},
codeurl={https://github.com/dmar-bonn/ipp-al},
doi={10.1109/IROS47612.2022.9981738}}

J. Rückin, L. Jin, and M. Popović, "Adaptive Informative Path Planning Using Deep Reinforcement Learning for UAV-based Active Sensing," in 2022 International Conference on Robotics and Automation (ICRA) , 2022, pp. 4473-4479. doi:10.1109/ICRA46639.2022.9812025
[BibTeX] [PDF] [Code]

@INPROCEEDINGS{9812025,
author={Rückin, Julius and Jin, Liren and Popović, Marija},
booktitle={2022 International Conference on Robotics and Automation (ICRA)},
title={Adaptive Informative Path Planning Using Deep Reinforcement Learning for UAV-based Active Sensing},
year={2022},
volume={},
number={},
pages={4473-4479},
url={https://arxiv.org/pdf/2109.13570.pdf},
codeurl={https://github.com/dmar-bonn/ipp-rl},
doi={10.1109/ICRA46639.2022.9812025}}

A. Dreier, H. Kuhlmann, and L. Klingbeil, "The potential of UAV-based Laser Scanning for Deformation Monitoring – Case Study on a Water Dam," in Proceedings of the 5th Joint International Symposium on Deformation Monitoring (JISDM) , Valencia, Spain, 2022. doi:10.4995/JISDM2022.2022.13833
[BibTeX]

@InProceedings{dreierdeformation,
title = {The potential of UAV-based Laser Scanning for Deformation Monitoring – Case Study on a Water Dam},
booktitle = {Proceedings of the 5th Joint International Symposium on Deformation Monitoring (JISDM)},
address = {Valencia, Spain},
year = {2022},
author = {Dreier, Ansgar and Kuhlmann, Heiner and Klingbeil, Lasse},
doi = {10.4995/JISDM2022.2022.13833}}

F. Esser, J. Moraga, L. Klingbeil, and H. Kuhlmann, "Accuracy improvement of mobile laser scanning point clouds using graph-based trajectory optimization," in Proceedings of the 5th Joint International Symposium on Deformation Monitoring (JISDM) , Valencia, Spain, 2022. doi:10.4995/JISDM2022.2022.13728
[BibTeX]

@InProceedings{essermobilelaser,
title = {Accuracy improvement of mobile laser scanning point clouds using graph-based trajectory optimization},
booktitle = {Proceedings of the 5th Joint International Symposium on Deformation Monitoring (JISDM)},
address = {Valencia, Spain},
year = {2022},
author = {Esser, Felix and Moraga, José and Klingbeil, Lasse and Kuhlmann, Heiner},
doi = {10.4995/JISDM2022.2022.13728}}

T. H. N. Ngyuen, M. Langensiepen, H. Hueging, T. Gaiser, S. J. Seidel, and F. Ewert, "Expansion and evaluation of two coupled root–shoot models in simulating CO2 and H2O fluxes and growth of maize," Vadose Zone Journal, vol. 21, 2022. doi:10.1002/vzj2.20181
[BibTeX] [PDF]

@article{Nguyen_vadose,
title = {Expansion and evaluation of two coupled root–shoot models in simulating CO2 and H2O fluxes and growth of maize},
journal = {Vadose Zone Journal},
volume = {21},
doi={10.1002/vzj2.20181},
issue = {3},
issn = {1539-1663},
year = {2022},
author = {Ngyuen, Thuy Huu Nguyen and Langensiepen, Matthias and Hueging, Hubert and Gaiser, Thomas and Seidel, Sabine J. and Ewert, Frank},
url = {https://acsess.onlinelibrary.wiley.com/doi/full/10.1002/vzj2.20181}
}

R. H. J. Heim, S. Streit, D. Koops, M. T. Kuska, and S. Paulus, "Digital Weed management – new trends for weed scoring in Sugar Beet," Sugar Industry, p. 343–351, 2022. doi:10.36961/si28804
[BibTeX]

@article{heim_streit_koops_kuska_paulus_2022, title={Digital Weed management – new trends for weed scoring in Sugar Beet}, DOI={10.36961/si28804}, journal={Sugar Industry}, author={Heim, René H.J. and Streit, Sebastian and Koops, Dirk and Kuska, Matheus Thomas and Paulus, Stefan}, year={2022}, pages={343–351}}

A. Mahlein, J. Behmann, D. Bohnenkamp, R. H. J. Heim, S. Streit, and S. Paulus, "Automated assessment of plant diseases and traits by sensors: how can digital technologies support smart farming and plant breeding?," in Advances in plant phenotyping for more sustainable crop production, A. Walter, Ed., Burleigh Dodds Science Publishing Limited, 2022, p. 351–372. doi:10.19103/as.2022.0102.17
[BibTeX] [PDF]

@inbook{mahlein_behmann_bohnenkamp_^heim_streit_paulus_2022,
place={Cambridge, UK},
doi={10.19103/as.2022.0102.17},
title={Automated assessment of plant diseases and traits by sensors: how can digital technologies support smart farming and plant breeding?},
booktitle={Advances in plant phenotyping for more sustainable crop production},
publisher={Burleigh Dodds Science Publishing Limited},
author={Mahlein, Anne-Katrin and Behmann, J. and Bohnenkamp, D. and Heim, R.H.J. and Streit, S. and Paulus, S.},
editor={Walter, AEditor},
year={2022},
pages={351–372},
url={https://www.researchgate.net/publication/362039221_Automated_assessment_of_plant_diseases_and_traits_by_sensors_how_can_digital_technologies_support_smart_farming_and_plant_breeding},}

M. Heep and E. Zell, "ShadowPatch: Shadow Based Segmentation for Reliable Depth Discontinuities in Photometric Stereo," Computer Graphics Forum, 2022. doi:10.1111/cgf.14707
[BibTeX] [PDF] [Video]

@article{HeepCGF2022,
title={ShadowPatch: Shadow Based Segmentation for Reliable Depth Discontinuities in Photometric Stereo},
author={Heep, Moritz and Zell, Eduard},
journal={Computer Graphics Forum},
year={2022},
videourl={https://www.youtube.com/watch?v=XYmJOfnWTHg},
url={http://www.eduardzell.com/publication/pg2022_paper/PG_2022_Photometric.pdf},
doi={10.1111/cgf.14707}}

Y. Pan, Y. Kompis, L. Bartolomei, R. Mascaro, C. Stachniss, and M. Chli, "Voxfield: Non-Projective Signed Distance Fields for Online Planning and 3D Reconstruction," in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022) , 2022. doi:10.3929/ethz-b-000560719
[BibTeX] [PDF] [Code] [Video]

@inproceedings{pan2022voxfield,
title={Voxfield: Non-Projective Signed Distance Fields for Online Planning and 3D Reconstruction},
author={Pan, Yue and Kompis, Yves and Bartolomei, Luca and Mascaro, Ruben and Stachniss, Cyrill and Chli, Margarita},
booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022)},
year={2022},
doi={10.3929/ethz-b-000560719},
codeurl={https://github.com/VIS4ROB-lab/voxfield},
videourl={https://www.youtube.com/watch?v=4HB4RXChrbg},
url={https://www.research-collection.ethz.ch/handle/20.500.11850/560719}}

K. D. Ingole, N. Nagarajan, S. Uhse, C. Giannini, and A. Djamei, "Tetracycline-controlled (TetON) gene expression system for the smut fungus Ustilago maydis," Frontiers in Fungal Biology, p. 65, 2022. doi:10.3389/ffunb.2022.1029114
[BibTeX] [PDF]

@article{ingole2022tetracycline,
title={Tetracycline-controlled (TetON) gene expression system for the smut fungus Ustilago maydis},
author={Ingole, Kishor D and Nagarajan, Nithya and Uhse, Simon and Giannini, Caterina and Djamei, Armin},
journal={Frontiers in Fungal Biology},
pages={65},
year={2022},
doi={10.3389/ffunb.2022.1029114},
url={https://www.frontiersin.org/articles/10.3389/ffunb.2022.1029114/full}}

A. Macabuhay, B. Arsova, M. Watt, K. A. Nagel, H. Lenz, A. Putz, S. Adels, M. Müller-Linow, J. Kelm, A. A. Johnson, and others, "Plant Growth Promotion and Heat Stress Amelioration in Arabidopsis Inoculated with Paraburkholderia phytofirmans PsJN Rhizobacteria Quantified with the GrowScreen-Agar II Phenotyping Platform," Plants, vol. 11, iss. 21, 2022. doi:10.3390/plants11212927
[BibTeX] [PDF]

@article{macabuhay2022plant,
title={Plant Growth Promotion and Heat Stress Amelioration in Arabidopsis Inoculated with Paraburkholderia phytofirmans PsJN Rhizobacteria Quantified with the GrowScreen-Agar II Phenotyping Platform},
author={Macabuhay, Allene and Arsova, Borjana and Watt, Michelle and Nagel, Kerstin A and Lenz, Henning and Putz, Alexander and Adels, Sascha and Müller-Linow, Mark and Kelm, Jana and Johnson, Alexander AT and others},
journal={Plants},
volume={11},
number={21},
year={2022},
doi={10.3390/plants11212927},
url={https://www.mdpi.com/2223-7747/11/21/2927}}

Y. Yu, J. A. Huisman, A. Klotzsche, H. Vereecken, and L. Weihermüller, "Coupled full-waveform inversion of horizontal borehole ground penetrating radar data to estimate soil hydraulic parameters: A synthetic study," Journal of Hydrology, vol. 610, 2022. doi:10.1016/j.jhydrol.2022.127817
[BibTeX] [PDF]

@article{yu2022coupled,
title={Coupled full-waveform inversion of horizontal borehole ground penetrating radar data to estimate soil hydraulic parameters: A synthetic study},
author={Yu, Yi and Huisman, Johan Alexander and Klotzsche, Anja and Vereecken, Harry and Weihermüller, Lutz},
journal={Journal of Hydrology},
volume={610},
year={2022},
doi={10.1016/j.jhydrol.2022.127817},
url={https://juser.fz-juelich.de/record/907342/files/HYDROL-S-21-04448.pdf}}

M. Schulz, D. Hofmann, B. Thiele, M. Rahmati, M. Siebers, V. Schütz, S. Jeong, L. Bigler, F. Held, B. Wu, and others, "A Below Ground Chemical Fight for Phosphate and Habitat-Interactions of Camelina sativa (L.) Crantz with Microorganisms," , 2022. doi:10.21203/rs.3.rs-2247394/v1
[BibTeX] [PDF]

@article{schulz2022below,
title={A Below Ground Chemical Fight for Phosphate and Habitat-Interactions of Camelina sativa (L.) Crantz with Microorganisms},
author={Schulz, Margot and Hofmann, Diana and Thiele, Björn and Rahmati, Mehdi and Siebers, Meike and Schütz, Vadim and Jeong, Seungwoo and Bigler, Laurent and Held, Federico and Wu, Bei and others},
year={2022},
doi={10.21203/rs.3.rs-2247394/v1},
url={https://www.researchsquare.com/article/rs-2247394/v1}}

C. Simanjuntak, T. Gaiser, H. E. Ahrends, and A. K. Srivastava, "Spatial and temporal patterns of agrometeorological indicators in maize producing provinces of South Africa," Scientific Reports, vol. 12, iss. 1, pp. 1-18, 2022. doi:10.1038/s41598-022-15847-7
[BibTeX] [PDF]

@article{simanjuntak2022spatial,
title={Spatial and temporal patterns of agrometeorological indicators in maize producing provinces of South Africa},
author={Simanjuntak, Christian and Gaiser, Thomas and Ahrends, Hella Ellen and Srivastava, Amit Kumar},
journal={Scientific Reports},
volume={12},
number={1},
pages={1-18},
year={2022},
doi={10.1038/s41598-022-15847-7},
url={https://www.nature.com/articles/s41598-022-15847-7}}

J. Bentz, R. A. Patel, P. Benard, A. Lieu, A. Haupenthal, and E. Kroener, "How Heterogeneous Pore Scale Distributions of Wettability Affect Infiltration into Porous Media," Water, vol. 14, iss. 7, p. 1110, 2022. doi:10.3390/w14071110
[BibTeX] [PDF]

@article{bentz2022,
title={How Heterogeneous Pore Scale Distributions of Wettability Affect Infiltration into Porous Media},
volume={14},
ISSN={2073-4441},
url={http://dx.doi.org/10.3390/w14071110},
DOI={10.3390/w14071110},
number={7},
journal={Water},
publisher={MDPI AG},
author={Bentz, Jonas and Patel, Ravi A. and Benard, Pascal and Lieu, Alice and Haupenthal, Adrian and Kroener, Eva},
year={2022},
pages={1110}}

S. Dueri, H. Brown, S. Asseng, F. Ewert, H. Webber, M. George, R. Craigie, J. R. Guarin, D. N. Pequeno, T. Stella, and others, "Simulation of winter wheat response to variable sowing dates and densities in a high-yielding environment," Journal of Experimental Botany, vol. 73, iss. 16, pp. 5715-5729, 2022. doi:10.1093/jxb/erac221
[BibTeX] [PDF]

@article{dueri2022simulation,
title={Simulation of winter wheat response to variable sowing dates and densities in a high-yielding environment},
author={Dueri, Sibylle and Brown, Hamish and Asseng, Senthold and Ewert, Frank and Webber, Heidi and George, Mike and Craigie, Rob and Guarin, Jose Rafael and Pequeno, Diego NL and Stella, Tommaso and others},
journal={Journal of Experimental Botany},
volume={73},
number={16},
pages={5715-5729},
year={2022},
doi={10.1093/jxb/erac221},
url={https://academic.oup.com/jxb/article/73/16/5715/6612782}}

M. P. J. Tabe-Ojong, B. H. Gebrekidan, E. Nshakira-Rukundo, J. Börner, and T. Heckelei, "COVID-19 in rural Africa: Food access disruptions, food insecurity and coping strategies in Kenya, Namibia, and Tanzania," Agricultural Economics, 2022. doi:10.1111/agec.12709
[BibTeX] [PDF]

@article{tabe2022covid,
title={COVID-19 in rural Africa: Food access disruptions, food insecurity and coping strategies in Kenya, Namibia, and Tanzania},
author={Tabe-Ojong, Martin Paul Jr and Gebrekidan, Bisrat Haile and Nshakira-Rukundo, Emmanuel and Börner, Jan and Heckelei, Thomas},
journal={Agricultural Economics},
year={2022},
doi={10.1111/agec.12709},
url={https://onlinelibrary.wiley.com/doi/10.1111/agec.12709}}

E. Cisneros, J. Börner, S. Pagiola, and S. Wunder, "Impacts of conservation incentives in protected areas: The case of Bolsa Floresta, Brazil," Journal of Environmental Economics and Management, vol. 111, p. 102572, 2022. doi:10.1016/j.jeem.2021.102572
[BibTeX] [PDF]

@article{cisneros2022journal,
title={Impacts of conservation incentives in protected areas: The case of Bolsa Floresta, Brazil},
author={Cisneros, Elìas and Börner, Jan and Pagiola, Stefano and Wunder, Sven},
journal={Journal of Environmental Economics and Management},
volume={111},
pages={102572},
year={2022},
doi={10.1016/j.jeem.2021.102572},
url={https://www.sciencedirect.com/science/article/pii/S0095069621001200?via%3Dihub}}

A. Raza, H. Ahrends, M. Habib-ur-Rahman, H. Hüging, and T. Gaiser, "Using the Taguchi experimental design for assessing within-field variability of surface run-off and soil erosion risk," Science of The Total Environment, vol. 828, p. 154567, 2022. doi:10.1016/j.scitotenv.2022.154567
[BibTeX] [PDF]

@article{raza2022using,
title={Using the Taguchi experimental design for assessing within-field variability of surface run-off and soil erosion risk},
author={Raza, Ahsan and Ahrends, Hella and Habib-ur-Rahman, Muhammad and Hüging, Hubert and Gaiser, Thomas},
journal={Science of The Total Environment},
volume={828},
pages={154567},
year={2022},
doi={10.1016/j.scitotenv.2022.154567},
url={https://helda.helsinki.fi/server/api/core/bitstreams/ccfc2d73-ad76-4407-9b58-639d94766332/content}}

U. Ploll, M. Arato, J. Börner, and M. Hartmann, "Sustainable Innovations: A Qualitative Study on Farmers’ Perceptions Driving the Diffusion of Beneficial Soil Microbes in Germany and the UK," Sustainability, vol. 14, iss. 10, p. 5749, 2022. doi:10.3390/su14105749
[BibTeX] [PDF]

@article{ploll2022sustainable,
title={Sustainable Innovations: A Qualitative Study on Farmers’ Perceptions Driving the Diffusion of Beneficial Soil Microbes in Germany and the UK},
author={Ploll, Ursula and Arato, Miguel and Börner, Jan and Hartmann, Monika},
journal={Sustainability},
volume={14},
number={10},
pages={5749},
year={2022},
doi={10.3390/su14105749},
url={https://www.mdpi.com/2071-1050/14/10/5749}}

M. T. Kuska, R. Heim, I. Geedicke, K. M. Gold, A. Brugger, and S. Paulus, "Digital plant pathology: a foundation and guide to modern agriculture," Journal of Plant Diseases and Protection, pp. 1-12, 2022. doi:10.1007/s41348-022-00600-z
[BibTeX] [PDF]

@article{kuska2022digital,
title={Digital plant pathology: a foundation and guide to modern agriculture},
author={Kuska, Matheus Thomas and Heim, René and Geedicke, Ina and Gold, Kaitlin M and Brugger, Anna and Paulus, Stefan},
journal={Journal of Plant Diseases and Protection},
pages={1-12},
year={2022},
doi={10.1007/s41348-022-00600-z},
url={https://link.springer.com/article/10.1007/s41348-022-00600-z}}

Quezada-Martinez D., J. Zou, W. Zhang, A. S. Mason, and others, "Allele segregation analysis of F1 hybrids between independent Brassica allohexaploid lineages," Chromosoma, vol. 131, p. 147–161, 2022. doi:10.1007/s00412-022-00774-3
[BibTeX] [PDF]

@article{quezada2022allele,
title={Allele segregation analysis of F1 hybrids between independent Brassica allohexaploid lineages},
author={Quezada-Martinez, D., and Zou, J. and Zhang, W. and Mason, A. S. and others},
journal={Chromosoma},
volume={131},
pages={147–161},
year={2022},
doi={10.1007/s00412-022-00774-3},
url={https://link.springer.com/article/10.1007/s00412-022-00774-3}}

C. Pahmeyer, "Advances in technology evaluation and decision support studies using bio-economic farm models," PhD Thesis, 2022. doi:10.5281/zenodo.4765941
[BibTeX] [PDF] [Video]

@phdthesis{pahmeyer2022advances,
title={Advances in technology evaluation and decision support studies using bio-economic farm models},
author={Pahmeyer, Christoph},
doi={10.5281/zenodo.4765941},
year={2022},
videourl={https://www.youtube.com/watch?v=9YaZm6a0eJI},
url={https://bonndoc.ulb.uni-bonn.de/xmlui/handle/20.500.11811/9746}}

L. Zhuang, A. Schnepf, K. Unger, Z. Liang, and R. Bol, "Home-Field Advantage of Litter Decomposition Faded 8 Years after Spruce Forest Clearcutting in Western Germany," Soil Systems, vol. 6, iss. 1, p. 26, 2022. doi:10.3390/soilsystems6010026
[BibTeX] [PDF]

@article{zhuang2022home,
title={Home-Field Advantage of Litter Decomposition Faded 8 Years after Spruce Forest Clearcutting in Western Germany},
author={Zhuang, Liyan and Schnepf, Andrea and Unger, Kirsten and Liang, Ziyi and Bol, Roland},
journal={Soil Systems},
volume={6},
number={1},
pages={26},
year={2022},
doi={10.3390/soilsystems6010026},
url={https://www.mdpi.com/2571-8789/6/1/26}}

N. Senapati, M. A. Semenov, N. G. Halford, M. J. Hawkesford, S. Asseng, M. Cooper, F. Ewert, M. K. van Ittersum, P. Martre, J. E. Olesen, and others, "Global wheat production could benefit from closing the genetic yield gap," Nature Food, vol. 3, iss. 7, pp. 532-541, 2022. doi:10.1038/s43016-022-00540-9
[BibTeX]

@article{senapati2022global,
title={Global wheat production could benefit from closing the genetic yield gap},
author={Senapati, Nimai and Semenov, Mikhail A and Halford, Nigel G and Hawkesford, Malcolm J and Asseng, Senthold and Cooper, Mark and Ewert, Frank and van Ittersum, Martin K and Martre, Pierre and Olesen, Jorgen E and others},
journal={Nature Food},
volume={3},
number={7},
pages={532-541},
year={2022},
doi={10.1038/s43016-022-00540-9}
}

M. Fayyaz, S. A. Koohpayegani, F. R. Jafari, S. Sengupta, H. R. V. Joze, E. Sommerlade, H. Pirsiavash, and J. Gall, "Adaptive token sampling for efficient vision transformers," in European Conference on Computer Vision , 2022, pp. 396-414. doi:10.1007/978-3-031-20083-0_24
[BibTeX] [PDF] [Code] [Video]

@inproceedings{fayyaz2022adaptive,
title={Adaptive token sampling for efficient vision transformers},
author={Fayyaz, Mohsen and Koohpayegani, Soroush Abbasi and Jafari, Farnoush Rezaei and Sengupta, Sunando and Joze, Hamid Reza Vaezi and Sommerlade, Eric and Pirsiavash, Hamed and Gall, Jürgen},
booktitle={European Conference on Computer Vision},
pages={396-414},
year={2022},
codeurl={https://github.com/adaptivetokensampling/ATS},
Videourl={https://www.youtube.com/watch?v=NJvyCcRjMfg},
doi={10.1007/978-3-031-20083-0_24},
url={https://pages.iai.uni-bonn.de/gall_juergen/download/jgall_adaptivetoken_eccv22.pdf}}

O. Dombrowski, C. Brogi, H. Hendricks Franssen, D. Zanotelli, and H. Bogena, "CLM5-FruitTree: a new sub-model for deciduous fruit trees in the Community Land Model (CLM5)," Geoscientific Model Development, vol. 15, iss. 13, pp. 5167-5193, 2022. doi:10.5194/gmd-15-5167-2022
[BibTeX] [PDF]

@article{dombrowski2022clm5,
title={CLM5-FruitTree: a new sub-model for deciduous fruit trees in the Community Land Model (CLM5)},
author={Dombrowski, Olga and Brogi, Cosimo and Hendricks Franssen, Harrie-Jan and Zanotelli, Damiano and Bogena, Heye},
journal={Geoscientific Model Development},
volume={15},
number={13},
pages={5167-5193},
year={2022},
doi={10.5194/gmd-15-5167-2022},
url={https://gmd.copernicus.org/articles/15/5167/2022/}}

V. Sushko, E. Schönfeld, D. Zhang, J. Gall, B. Schiele, and A. Khoreva, "OASIS: Only Adversarial Supervision for Semantic Image Synthesis," International Journal of Computer Vision, vol. 130, iss. 12, pp. 2903-2923, 2022. doi:10.1007/s11263-022-01673-x
[BibTeX] [PDF] [Code]

@article{sushko2022oasis,
title={OASIS: Only Adversarial Supervision for Semantic Image Synthesis},
author={Sushko, Vadim and Schönfeld, Edgar and Zhang, Dan and Gall, Jürgen and Schiele, Bernt and Khoreva, Anna},
journal={International Journal of Computer Vision},
volume={130},
number={12},
pages={2903-2923},
year={2022},
doi={10.1007/s11263-022-01673-x},
codeurl={https://github.com/boschresearch/OASIS},
url={https://link.springer.com/article/10.1007/s11263-022-01673-x}}

A. K. Srivastava, N. Safaei, S. Khaki, G. Lopez, W. Zeng, F. Ewert, T. Gaiser, and J. Rahimi, "Winter wheat yield prediction using convolutional neural networks from environmental and phenological data," Scientific Reports, vol. 12, iss. 1, pp. 1-14, 2022. doi:10.1038/s41598-022-06249-w
[BibTeX] [PDF]

@article{srivastava2022winter,
title={Winter wheat yield prediction using convolutional neural networks from environmental and phenological data},
author={Srivastava, Amit Kumar and Safaei, Nima and Khaki, Saeed and Lopez, Gina and Zeng, Wenzhi and Ewert, Frank and Gaiser, Thomas and Rahimi, Jaber},
journal={Scientific Reports},
volume={12},
number={1},
pages={1-14},
year={2022},
doi={10.1038/s41598-022-06249-w},
url={https://www.nature.com/articles/s41598-022-06249-w}}

R. Wehrle, G. Coulouma, and S. Pätzold, "Portable mid-infrared spectroscopy to predict parameters related to carbon storage in vineyard soils: Model calibrations under varying geopedological conditions," Biosystems Engineering, vol. 222, pp. 1-14, 2022. doi:10.1016/j.biosystemseng.2022.07.012
[BibTeX] [PDF]

@article{wehrle2022portable,
title={Portable mid-infrared spectroscopy to predict parameters related to carbon storage in vineyard soils: Model calibrations under varying geopedological conditions},
author={Wehrle, Ralf and Coulouma, Guillaume and Pätzold, Stefan},
journal={Biosystems Engineering},
volume={222},
pages={1-14},
year={2022},
doi={10.1016/j.biosystemseng.2022.07.012},
url={https://www.sciencedirect.com/science/article/pii/S153751102200174X}}

F. He, B. Thiele, T. Kraska, U. Schurr, and A. J. Kuhn, "Effects of Root Temperature and Cluster Position on Fruit Quality of Two Cocktail Tomato Cultivars," Agronomy, vol. 12, iss. 6, pp. 12-75, 2022. doi:10.3390/agronomy12061275
[BibTeX] [PDF]

@article{he2022effects,
title={Effects of Root Temperature and Cluster Position on Fruit Quality of Two Cocktail Tomato Cultivars},
author={He, Fang and Thiele, Björn and Kraska, Thorsten and Schurr, Ulrich and Kuhn, Arnd Jürgen},
journal={Agronomy},
volume={12},
number={6},
pages={12-75},
year={2022},
doi={10.3390/agronomy12061275},
url={https://www.mdpi.com/2073-4395/12/6/1275}}

M. Meyer, C. Hulke, J. Kamwi, H. Kolem, and J. Börner, "Spatially heterogeneous effects of collective action on environmental dependence in Namibia’s Zambezi region," World Development, vol. 159, pp. 106-142, 2022. doi:10.1016/j.worlddev.2022.106042
[BibTeX] [PDF]

@article{meyer2022spatially,
title={Spatially heterogeneous effects of collective action on environmental dependence in Namibia’s Zambezi region},
author={Meyer, Maximilian and Hulke, Carolin and Kamwi, Jonathan and Kolem, Hannah and Börner, Jan},
journal={World Development},
volume={159},
pages={106-142},
year={2022},
doi={10.1016/j.worlddev.2022.106042},
url={https://www.sciencedirect.com/science/article/pii/S0305750X22002327}}

F. S. Martinelli, L. Biber-Freudenberger, G. Stein, and J. Börner, "Will Brazil’s push for low-carbon biofuels contribute to achieving the SDGs? A systematic expert-based assessment," Cleaner Environmental Systems, vol. 5, pp. 100-175, 2022. doi:10.1016/j.cesys.2022.100075
[BibTeX] [PDF]

@article{martinelli2022will,
title={Will Brazil’s push for low-carbon biofuels contribute to achieving the SDGs? A systematic expert-based assessment},
author={Martinelli, Fernanda Silva and Biber-Freudenberger, Lisa and Stein, Guilherme and Börner, Jan},
journal={Cleaner Environmental Systems},
volume={5},
pages={100-175},
year={2022},
doi={10.1016/j.cesys.2022.100075},
url={https://www.sciencedirect.com/science/article/pii/S266678942200006X}}

E. Zell and R. McDonnell, "Compact Facial Landmark Layouts for Performance Capture," Computer Graphics Forum, vol. 41, iss. 2, pp. 121-133, 2022. doi:10.1111/cgf.14463
[BibTeX] [PDF]

@article {10.1111:cgf.14463,
journal = {Computer Graphics Forum},
title = {{Compact Facial Landmark Layouts for Performance Capture}},
author = {Zell, Eduard and McDonnell, Rachel},
year = {2022},
volume={41},
number={2},
pages={121-133},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.14463},
url={http://eduardzell.com/publication/eg2022_paper/EG22_Compact_Layouts_Zell.pdf}
}

B. H. Gebrekidan, T. Heckelei, and S. Rasch, "Modeling intensification decisions in the Kilombero Valley floodplain: A Bayesian belief network approach," Agricultural Economics, 2022. doi:10.1111/agec.12740
[BibTeX] [PDF]

@article{gebrekidan2022modeling,
title={Modeling intensification decisions in the Kilombero Valley floodplain: A Bayesian belief network approach},
author={Gebrekidan, Bisrat Haile and Heckelei, Thomas and Rasch, Sebastian},
journal={Agricultural Economics},
year={2022},
doi={10.1111/agec.12740},
url={https://onlinelibrary.wiley.com/doi/10.1111/agec.12740}}

C. Lenz and S. Behnke, "Bimanual telemanipulation with force and haptic feedback through an anthropomorphic avatar system," Robotics and Autonomous Systems, pp. 104-338, 2022. doi:10.1016/j.robot.2022.104338
[BibTeX]

@article{lenz2022bimanual,
title={Bimanual telemanipulation with force and haptic feedback through an anthropomorphic avatar system},
author={Lenz, Christian and Behnke, Sven},
journal={Robotics and Autonomous Systems},
pages={104-338},
year={2022},
doi={10.1016/j.robot.2022.104338}}

M. Meyer and J. Börner, "Rural livelihoods, community-based conservation, and human-wildlife conflict: Scope for synergies?," Biological Conservation, vol. 272, pp. 109-666, 2022. doi:10.1016/j.biocon.2022.109666
[BibTeX] [PDF]

@article{meyer2022rural,
title={Rural livelihoods, community-based conservation, and human-wildlife conflict: Scope for synergies?},
author={Meyer, Maximilian and Börner, Jan},
journal={Biological Conservation},
volume={272},
pages={109-666},
year={2022},
doi={10.1016/j.biocon.2022.109666},
url={https://www.sciencedirect.com/science/article/pii/S0006320722002191}}

L. Zhang, S. Poll, and S. Kollet, "Large-eddy Simulation of Soil Moisture Heterogeneity Induced Secondary Circulation with Ambient Winds," Quarterly Journal of the Royal Meteorological Society, 2022. doi:10.5194/egusphere-egu22-5533
[BibTeX] [PDF] [Video]

@article{zhanglarge,
title={Large-eddy Simulation of Soil Moisture Heterogeneity Induced Secondary Circulation with Ambient Winds},
author={Zhang, Lijie and Poll, Stefan and Kollet, Stefan},
journal={Quarterly Journal of the Royal Meteorological Society},
doi={10.5194/egusphere-egu22-5533},
year={2022},
videourl={https://www.youtube.com/watch?v=gHIbhRjnIJI},
url={https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/qj.4413}}

Y. Damm, E. Cisneros, and J. Börner, "Beyond Deforestation Reductions: Public Disclosure, Land-Use Change and Commodity Sourcing," SSRN, 2022. doi:10.2139/ssrn.4138439
[BibTeX] [PDF]

@article{damm2022beyond,
title={Beyond Deforestation Reductions: Public Disclosure, Land-Use Change and Commodity Sourcing},
author={Damm, Yannic and Cisneros, Elias and Börner, Jan},
doi={10.2139/ssrn.4138439},
journal={SSRN},
url={https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4138439},
year={2022}}

A. Massfeller, M. Meraner, S. Hüttel, and R. Uehleke, "Data on farmers’ acceptance of results-based agri-environmental schemes," Data in Brief, vol. 45, p. 108642, 2022. doi:10.1016/j.dib.2022.108642
[BibTeX] [PDF]

@article{massfeller2022data,
title={Data on farmers’ acceptance of results-based agri-environmental schemes},
author={Massfeller, Anna and Meraner, Manuela and Hüttel, Silke and Uehleke, Reinhard},
journal={Data in Brief},
volume={45},
pages={108642},
year={2022},
doi={10.1016/j.dib.2022.108642},
url={https://www.sciencedirect.com/science/article/pii/S2352340922008447}}

A. Schnepf, D. Leitner, G. Bodner, and M. Javaux, "Editorial: Benchmarking 3D-Models of Root Growth, Architecture and Functioning," Frontiers in Plant Science, vol. 13, 2022. doi:10.3389/fpls.2022.902587
[BibTeX] [PDF]

@article{schnepf2022benchmarking,
title={Editorial: Benchmarking 3D-Models of Root Growth, Architecture and Functioning},
author={Schnepf, Andrea and Leitner, Daniel and Bodner, Gernot and Javaux, Mathieu},
journal={Frontiers in Plant Science},
volume={13},
year={2022},
doi={10.3389/fpls.2022.902587},
url={https://www.frontiersin.org/articles/10.3389/fpls.2022.902587/full}}

M. Khan and A. Djamei, "Performing Infection Assays of Sporisorium reilianum f. sp. Zeae in Maize," in Environmental Responses in Plants, , 2022, pp. 291-298. doi:10.1007/978-1-0716-2297-1_20
[BibTeX] [PDF]

@incollection{khan2022performing,
title={Performing Infection Assays of Sporisorium reilianum f. sp. Zeae in Maize},
author={Khan, Mamoona and Djamei, Armin},
booktitle={Environmental Responses in Plants},
pages={291-298},
year={2022},
doi={10.1007/978-1-0716-2297-1_20},
url={https://www.phenorob.de/wp-content/uploads/2024/08/khan-and-Djamei-2022.pdf}}

H. Peng, M. P. Cendrero-Mateo, J. Bendig, B. Siegmann, K. Acebron, C. Kneer, K. Kataja, O. Muller, and U. Rascher, "HyScreen: A Ground-Based Imaging System for High-Resolution Red and Far-Red Solar-Induced Chlorophyll Fluorescence," Sensors, vol. 22, iss. 23, p. 9443, 2022. doi:10.3390/s22239443
[BibTeX] [PDF]

@article{peng2022hyscreen,
title={HyScreen: A Ground-Based Imaging System for High-Resolution Red and Far-Red Solar-Induced Chlorophyll Fluorescence},
author={Peng, Huaiyue and Cendrero-Mateo, Maria Pilar and Bendig, Juliane and Siegmann, Bastian and Acebron, Kelvin and Kneer, Caspar and Kataja, Kari and Muller, Onno and Rascher, Uwe},
journal={Sensors},
volume={22},
number={23},
pages={9443},
year={2022},
doi={10.3390/s22239443},
url={https://www.mdpi.com/1424-8220/22/23/9443}}

A. K. Srivast, T. Gaiser, A. S. Akinwumiju, W. Zeng, A. Ceglar, K. S. Ezui, F. Ewert, A. Adelodun, A. Adebayo, J. Sobamowo, M. Singh, and J. Rahimi, Simulating Regional Cassava Yield Gap in NigeriaResearch Square, 2022. doi:10.21203/rs.3.rs-1244050/v1
[BibTeX] [PDF]

Cassava production is essential for food security in Sub-Saharan Africa and serves as a major calorie- intake source in Nigeria. Here we use a crop model, LINTUL5, embedded into a modeling framework SIMPLACE to estimate potential cassava yield gaps (Yg) in 30 states of Nigeria. Our study of climate parameter influence on the variability of current and potential yields and Yg shows that cumulative radiation and precipitation were the most significant factors associated with cassava yield variability ( p = 0.01). The cumulative Yg mean was estimated as 18202 kg∙ha -1 , with a maximum of 31207 kg ha -1 in Kano state. Across the states, nutrient limitation accounts for 55.3% of the total cassava yield gap, while the remaining 44.7% is attributed to water limitation. The highest untapped water-limited yields were estimated in States, such as Bauchi, Gombe, and Sokoto, characterized by the short rainy season. Conclusively, the current cassava yield levels can be increased by a factor of five through soil fertility enhancement and with irrigation, particularly in semi-arid regions.

@misc {PPR:PPR441486,
Title = {Simulating Regional Cassava Yield Gap in Nigeria},
Author = {Srivast, Amit Kumar and Gaiser, Thomas and Akinwumiju, Akinola Shola and Zeng, Wenzhi and Ceglar, Andrej and Ezui, Kodjovi Senam and Ewert, Frank and Adelodun, Adedeji and Adebayo, Abass and Sobamowo, Jumoke and Singh, Manmeet and Rahimi, Jaber},
DOI = {10.21203/rs.3.rs-1244050/v1},
Abstract = {Cassava production is essential for food security in Sub-Saharan Africa and serves as a major calorie- intake source in Nigeria. Here we use a crop model, LINTUL5, embedded into a modeling framework SIMPLACE to estimate potential cassava yield gaps (Yg) in 30 states of Nigeria. Our study of climate parameter influence on the variability of current and potential yields and Yg shows that cumulative radiation and precipitation were the most significant factors associated with cassava yield variability ( p = 0.01). The cumulative Yg mean was estimated as 18202 kg∙ha -1 , with a maximum of 31207 kg ha -1 in Kano state. Across the states, nutrient limitation accounts for 55.3% of the total cassava yield gap, while the remaining 44.7% is attributed to water limitation. The highest untapped water-limited yields were estimated in States, such as Bauchi, Gombe, and Sokoto, characterized by the short rainy season. Conclusively, the current cassava yield levels can be increased by a factor of five through soil fertility enhancement and with irrigation, particularly in semi-arid regions.},
Publisher = {Research Square},
Year = {2022},
DOI = {10.21203/rs.3.rs-1244050/v1},
URL = {https://www.researchgate.net/publication/357753638_Simulating_Regional_Cassava_Yield_Gap_in_Nigeria},
}

X. Chen, B. Mersch, L. Nunes, R. Marcuzzi, I. Vizzo, J. Behley, and C. Stachniss, "Automatic Labeling to Generate Training Data for Online LiDAR-Based Moving Object Segmentation," ral, vol. 7, iss. 3, pp. 6107-6114, 2022. doi:10.1109/LRA.2022.3166544
[BibTeX] [PDF] [Code] [Video]

@article{chen2022ral,
author = {X. Chen and B. Mersch and L. Nunes and R. Marcuzzi and I. Vizzo and J. Behley and C. Stachniss},
title = {{Automatic Labeling to Generate Training Data for Online LiDAR-Based Moving Object Segmentation}},
journal = ral,
year = 2022,
volume = 7,
number = 3,
pages = {6107-6114},
url = {https://arxiv.org/pdf/2201.04501},
issn = {2377-3766},
doi = {10.1109/LRA.2022.3166544},
codeurl = {https://github.com/PRBonn/auto-mos},
videourl = {https://youtu.be/3V5RA1udL4c},
}

M. Miranda, L. Drees, and R. Roscher, "Controlled Multi-modal Image Generation for Plant Growth Modeling," in 2022 26th International Conference on Pattern Recognition (ICPR) , 2022, pp. 5118-5124. doi:10.1109/ICPR56361.2022.9956115
[BibTeX] [Video]

@inproceedings{miranda2022controlled,
title={Controlled Multi-modal Image Generation for Plant Growth Modeling},
author={Miranda, Miro and Drees, Lukas and Roscher, Ribana},
booktitle={2022 26th International Conference on Pattern Recognition (ICPR)},
pages={5118-5124},
year={2022},
videourl={https://www.youtube.com/watch?v=S4zu5fdMtho},
doi={10.1109/ICPR56361.2022.9956115}}

T. Koch, D. Deumlich, P. Chifflard, K. Panten, and K. Grahmann, "Using model simulation to evaluate soil loss potential in diversified agricultural landscapes," European Journal of Soil Science, p. e13332, 2022. doi:https://doi.org/10.1111/ejss.13332
[BibTeX] [PDF]

@article{kochusing,
title={Using model simulation to evaluate soil loss potential in diversified agricultural landscapes},
author={Koch, Tobias and Deumlich, Detlef and Chifflard, Peter and Panten, Kerstin and Grahmann, Kathrin},
journal={European Journal of Soil Science},
pages={e13332},
year={2022},
doi={ https://doi.org/10.1111/ejss.13332},
url={https://bsssjournals.onlinelibrary.wiley.com/doi/10.1111/ejss.13332}}

T. A. West, J. L. Caviglia-Harris, F. S. Martins, D. E. Silva, and J. Börner, "Potential conservation gains from improved protected area management in the Brazilian Amazon," Biological Conservation, vol. 269, p. 109526, 2022. doi:10.1016/j.biocon.2022.109526
[BibTeX] [PDF]

@article{west2022potential,
title={Potential conservation gains from improved protected area management in the Brazilian Amazon},
author={West, Thales AP and Caviglia-Harris, Jill L and Martins, Flora SRV and Silva, Daniel E and Börner, Jan},
journal={Biological Conservation},
volume={269},
pages={109526},
year={2022},
doi={10.1016/j.biocon.2022.109526},
url={https://www.sciencedirect.com/science/article/pii/S0006320722000799}}

E. I. Katche, A. Schierholt, H. C. Becker, J. Batley, and A. S. Mason, "Fertility, genome stability, and homozygosity in a diverse set of resynthesized rapeseed lines," The Crop Journal, 2022. doi:10.1016/j.cj.2022.07.022
[BibTeX] [PDF]

@article{katche2022fertility,
title={Fertility, genome stability, and homozygosity in a diverse set of resynthesized rapeseed lines},
author={Katche, Elizabeth Ihien and Schierholt, Antje and Becker, Heiko C and Batley, Jacqueline and Mason, Annaliese S},
journal={The Crop Journal},
year={2022},
doi={10.1016/j.cj.2022.07.022},
url={https://www.sciencedirect.com/science/article/pii/S2214514122002082}}

L. Drees, I. Weber, M. Russwurm, and R. Roscher, "Time Dependent Image Generation of Plants from Incomplete Sequences with CNN-Transformer," in DAGM German Conference on Pattern Recognition , 2022, pp. 495-510. doi:10.1007/978-3-031-16788-1_30
[BibTeX] [Code] [Video]

@inproceedings{drees2022time,
title={Time Dependent Image Generation of Plants from Incomplete Sequences with CNN-Transformer},
author={Drees, Lukas and Weber, Immanuel and Russwurm, Marc and Roscher, Ribana},
booktitle={DAGM German Conference on Pattern Recognition},
pages={495-510},
year={2022},
codeurl={https://github.com/luked12/transgrow},
videourl={https://www.youtube.com/watch?v=F2lmLkDrovw},
doi={10.1007/978-3-031-16788-1_30}}

R. S. de Nóia Júnior, F. Ewert, H. Webber, P. Martre, T. W. Hertel, M. K. van Ittersum, and S. Asseng, "Needed global wheat stock and crop management in response to the war in Ukraine," Global Food Security, vol. 35, p. 100662, 2022. doi:10.1016/j.gfs.2022.100662
[BibTeX]

The war in Ukraine threatened to block 9% of global wheat exports, driving wheat prices to unprecedented heights. We advocate, that in the short term, compensating for such an export shortage will require a coordinated release of wheat stocks, while if the export block persists, other export countries will need to fill the gap by increasing wheat yields or by expanding wheat cropping areas by 8% in aggregate. We estimate that a production increase would require an extra half a million tons of nitrogen fertilizer, yet fertilizer prices are at record levels, driven by rising energy prices. Year-to-year variability plus more frequent climate change-induced crop failures could additionally reduce exports by another 5 to 7 million tons in any given year, further stressing global markets. Without stabilizing wheat supplies through judicious management of stocks and continuing yield improvements, food and national security are at risk across many nations in the world.

@article{NOIAJUNIOR2022100662,
title = {Needed global wheat stock and crop management in response to the war in Ukraine},
journal = {Global Food Security},
volume = {35},
pages = {100662},
year = {2022},
issn = {2211-9124},
doi = {10.1016/j.gfs.2022.100662},
author = {Rogério de S. {Nóia Júnior} and Frank Ewert and Heidi Webber and Pierre Martre and Thomas W. Hertel and Martin K. {van Ittersum} and Senthold Asseng},
keywords = {Food security, Hunger, Ukraine, War, Wheat export},
abstract = {The war in Ukraine threatened to block 9% of global wheat exports, driving wheat prices to unprecedented heights. We advocate, that in the short term, compensating for such an export shortage will require a coordinated release of wheat stocks, while if the export block persists, other export countries will need to fill the gap by increasing wheat yields or by expanding wheat cropping areas by 8% in aggregate. We estimate that a production increase would require an extra half a million tons of nitrogen fertilizer, yet fertilizer prices are at record levels, driven by rising energy prices. Year-to-year variability plus more frequent climate change-induced crop failures could additionally reduce exports by another 5 to 7 million tons in any given year, further stressing global markets. Without stabilizing wheat supplies through judicious management of stocks and continuing yield improvements, food and national security are at risk across many nations in the world.}}

A. Bonerath, L. Temerowski, S. Gedicke, and J. -H. Haunert, "Exploring Spatio-Temporal Event Data on a Smart Watch," Abstracts of the ICA, vol. 5, p. 96, 2022. doi:10.5194/ica-abs-5-96-2022
[BibTeX] [PDF]

@Article{ica-abs-5-96-2022,
AUTHOR = {Bonerath, A. and Temerowski, L. and Gedicke, S. and Haunert, J.-H.},
TITLE = {Exploring Spatio-Temporal Event Data on a Smart Watch},
JOURNAL = {Abstracts of the ICA},
VOLUME = {5},
YEAR = {2022},
PAGES = {96},
URL = {https://ica-abs.copernicus.org/articles/5/96/2022/},
DOI = {10.5194/ica-abs-5-96-2022}
}

L. Wiesmann, T. Guadagnino, I. Vizzo, G. Grisetti, J. Behley, and C. Stachniss, "DCPCR: Deep Compressed Point Cloud Registration in Large-Scale Outdoor Environments," IEEE Robotics and Automation Letters, vol. 7, iss. 3, pp. 6327-6334, 2022. doi:10.1109/LRA.2022.3171068
[BibTeX] [PDF] [Code] [Video]

@ARTICLE{9765365,
author={Wiesmann, Louis and Guadagnino, Tiziano and Vizzo, Ignacio and Grisetti, Giorgio and Behley, Jens and Stachniss, Cyrill},
journal={IEEE Robotics and Automation Letters},
title={DCPCR: Deep Compressed Point Cloud Registration in Large-Scale Outdoor Environments},
year={2022},
volume={7},
number={3},
pages={6327-6334},
doi={10.1109/LRA.2022.3171068},
codeurl={https://github.com/PRBonn/DCPCR},
videourl={https://www.youtube.com/watch?v=RqLr2RTGy1s},
url={ https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/wiesmann2022ral-iros.pdf}}

I. Vizzo, B. Mersch, R. Marcuzzi, L. Wiesmann, J. Behley, and C. Stachniss, "Make it Dense: Self-Supervised Geometric Scan Completion of Sparse 3D LiDAR Scans in Large Outdoor Environments," IEEE Robotics and Automation Letters, vol. 7, iss. 3, pp. 8534-8541, 2022. doi:10.1109/LRA.2022.3187255
[BibTeX] [PDF] [Code] [Video]

@ARTICLE{9812507,
author={Vizzo, Ignacio and Mersch, Benedikt and Marcuzzi, Rodrigo and Wiesmann, Louis and Behley, Jens and Stachniss, Cyrill},
journal={IEEE Robotics and Automation Letters},
title={Make it Dense: Self-Supervised Geometric Scan Completion of Sparse 3D LiDAR Scans in Large Outdoor Environments},
year={2022},
volume={7},
number={3},
pages={8534-8541},
doi={10.1109/LRA.2022.3187255},
codeurl={https://github.com/PRBonn/make_it_dense
},
videourl={https://www.youtube.com/watch?v=NVjURcArHn8
},
url={https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/vizzo2022ral-iros.pdf}}

T. Guadagnino, X. Chen, M. Sodano, J. Behley, G. Grisetti, and C. Stachniss, "Fast Sparse LiDAR Odometry Using Self-Supervised Feature Selection on Intensity Images," IEEE Robotics and Automation Letters, vol. 7, iss. 3, pp. 7597-7604, 2022. doi:10.1109/LRA.2022.3184454
[BibTeX] [PDF]

@ARTICLE{9801638,
author={Guadagnino, Tiziano and Chen, Xieyuanli and Sodano, Matteo and Behley, Jens and Grisetti, Giorgio and Stachniss, Cyrill},
journal={IEEE Robotics and Automation Letters},
title={Fast Sparse LiDAR Odometry Using Self-Supervised Feature Selection on Intensity Images},
year={2022},
volume={7},
number={3},
pages={7597-7604},
doi={10.1109/LRA.2022.3184454},
url={https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/guadagnino2022ral-iros.pdf}}

J. Leonhardt, L. Drees, P. Jung, and R. Roscher, "Probabilistic Biomass Estimation with Conditional Generative Adversarial Networks," in Pattern Recognition , 2022, pp. 479-494. doi:10.1007/978-3-031-16788-1_29
[BibTeX] [PDF]

Biomass is an important variable for our understanding of the terrestrial carbon cycle, facilitating the need for satellite-based global and continuous monitoring. However, current machine learning methods used to map biomass can often not model the complex relationship between biomass and satellite observations or cannot account for the estimation's uncertainty. In this work, we exploit the stochastic properties of Conditional Generative Adversarial Networks for quantifying aleatoric uncertainty. Furthermore, we use generator Snapshot Ensembles in the context of epistemic uncertainty and show that unlabeled data can easily be incorporated into the training process. The methodology is tested on a newly presented dataset for satellite-based estimation of biomass from multispectral and radar imagery, using lidar-derived maps as reference data. The experiments show that the final network ensemble captures the dataset's probabilistic characteristics, delivering accurate estimates and well-calibrated uncertainties.

@InProceedings{10.1007/978-3-031-16788-1_29,
author= {Leonhardt, Johannes and Drees, Lukas and Jung, Peter and Roscher, Ribana},
editor= {Andres, Björn and Bernard, Florian and Cremers, Daniel and Frintrop, Simone and Goldlücke, Bastian and Ihrke, Ivo},
title= {Probabilistic Biomass Estimation with Conditional Generative Adversarial Networks},
booktitle= {Pattern Recognition},
year= {2022},
publisher= {Springer International Publishing},
pages= {479-494},
abstract= {Biomass is an important variable for our understanding of the terrestrial carbon cycle, facilitating the need for satellite-based global and continuous monitoring. However, current machine learning methods used to map biomass can often not model the complex relationship between biomass and satellite observations or cannot account for the estimation's uncertainty. In this work, we exploit the stochastic properties of Conditional Generative Adversarial Networks for quantifying aleatoric uncertainty. Furthermore, we use generator Snapshot Ensembles in the context of epistemic uncertainty and show that unlabeled data can easily be incorporated into the training process. The methodology is tested on a newly presented dataset for satellite-based estimation of biomass from multispectral and radar imagery, using lidar-derived maps as reference data. The experiments show that the final network ensemble captures the dataset's probabilistic characteristics, delivering accurate estimates and well-calibrated uncertainties.},
isbn= {978-3-031-6788-1},
doi= {10.1007/978-3-031-16788-1_29},
url= {https://www.phenorob.de/wp-content/uploads/2024/08/leonhardt2022probabilistic.pdf}}

F. Magistri, E. Marks, S. Nagulavancha, I. Vizzo, T. Läbe, J. Behley, M. Halstead, C. McCool, and C. Stachniss, "Contrastive 3D Shape Completion and Reconstruction for Agricultural Robots Using RGB-D Frames." 2022, pp. 10120-10127. doi:10.1109/LRA.2022.3193239
[BibTeX] [PDF] [Video]

@inproceedings{marks2022precise,
author= {Magistri, Federico and Marks, Elias and Nagulavancha, Sumanth and Vizzo, Ignacio and Läbe, Thomas and Behley, Jens and Halstead, Michael and McCool, Chris and Stachniss, Cyrill},
journal= {IEEE Robotics and Automation Letters},
title= {Contrastive 3D Shape Completion and Reconstruction for Agricultural Robots Using RGB-D Frames},
year= {2022},
volume= {7},
number= {4},
pages= {10120-10127},
doi= {10.1109/LRA.2022.3193239},
videourl= {https://www.youtube.com/watch?v=2ErUf9q7YOI
},
url= {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/magistri2022ral-iros.pdf}}

Y. Kusunose, J. J. Rossi, D. A. Van Sanford, P. D. Alderman, J. A. Anderson, Y. Chai, M. K. Gerullis, K. S. V. Jagadish, P. A. Paul, J. B. Tack, and B. D. Wright, "Sustaining productivity gains in the face of climate change: A research agenda for US wheat," Global Change Biology, 2022. doi:10.1111/gcb.16538
[BibTeX] [PDF]

Abstract Wheat is a globally important crop and one of the “big three” US field crops. But unlike the other two (maize and soybean), in the United States its development is commercially unattractive, and so its breeding takes place primarily in public universities. Troublingly, the incentive structures within these universities may be hindering genetic improvement just as climate change is complicating breeding efforts. “Business as usual” in the US public wheat-breeding infrastructure may not sustain productivity increases. To address this concern, we held a multidisciplinary conference in which researchers from 12 US (public) universities and one European university shared the current state of knowledge in their disciplines, aired concerns, and proposed initiatives that could facilitate maintaining genetic improvement of wheat in the face of climate change. We discovered that climate-change-oriented breeding efforts are currently considered too risky and/or costly for most university wheat breeders to undertake, leading to a relative lack of breeding efforts that focus on abiotic stressors such as drought and heat. We hypothesize that this risk/cost burden can be reduced through the development of appropriate germplasm, relevant screening mechanisms, consistent germplasm characterization, and innovative models predicting the performance of germplasm under projected future climate conditions. However, doing so will require coordinated, longer-term, inter-regional efforts to generate phenotype data, and the modification of incentive structures to consistently reward such efforts.

@article{https://doi.org/10.1111/gcb.16538,
author = {Kusunose, Yoko and Rossi, Jairus J. and Van Sanford, David A. and Alderman, Phillip D. and Anderson, James A. and Chai, Yuan and Gerullis, Maria K. and Jagadish, S. V. Krishna and Paul, Pierce A. and Tack, Jesse B. and Wright, Brian D.},
title = {Sustaining productivity gains in the face of climate change: A research agenda for US wheat},
journal = {Global Change Biology},
year = {2022},
keywords = {abiotic stressors, biotic stressors, climate uncertainty, genetic improvement, institutions, land-grant universities, research infrastructure, United States, wheat breeding},
doi = {10.1111/gcb.16538},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.16538},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/gcb.16538},
abstract = {Abstract Wheat is a globally important crop and one of the “big three” US field crops. But unlike the other two (maize and soybean), in the United States its development is commercially unattractive, and so its breeding takes place primarily in public universities. Troublingly, the incentive structures within these universities may be hindering genetic improvement just as climate change is complicating breeding efforts. “Business as usual” in the US public wheat-breeding infrastructure may not sustain productivity increases. To address this concern, we held a multidisciplinary conference in which researchers from 12 US (public) universities and one European university shared the current state of knowledge in their disciplines, aired concerns, and proposed initiatives that could facilitate maintaining genetic improvement of wheat in the face of climate change. We discovered that climate-change-oriented breeding efforts are currently considered too risky and/or costly for most university wheat breeders to undertake, leading to a relative lack of breeding efforts that focus on abiotic stressors such as drought and heat. We hypothesize that this risk/cost burden can be reduced through the development of appropriate germplasm, relevant screening mechanisms, consistent germplasm characterization, and innovative models predicting the performance of germplasm under projected future climate conditions. However, doing so will require coordinated, longer-term, inter-regional efforts to generate phenotype data, and the modification of incentive structures to consistently reward such efforts.}
}

N. Wang, B. Siegmann, U. Rascher, J. G. P. W. Clevers, O. Muller, H. Bartholomeus, J. Bendig, D. Masiliunas, R. Pude, and L. Kooistra, "Comparison of a UAV- and an airborne-based system to acquire far-red sun-induced chlorophyll fluorescence measurements over structurally different crops," Agricultural and Forest Meteorology, vol. 323, 2022. doi:10.1016/j.agrformet.2022.109081
[BibTeX] [PDF]

@article{wang_meteorology,
author = {Wang, Na and Siegmann, Bastian and Rascher, Uwe and Clevers, Jan G.P.W. and Muller, Onno and Bartholomeus, Harm and Bendig, Juliane and Masiliunas, Dainius and Pude, Ralf and Kooistra, Lammert},
title = {Comparison of a UAV- and an airborne-based system to acquire far-red sun-induced chlorophyll fluorescence measurements over structurally different crops},
journal = {Agricultural and Forest Meteorology},
volume = {323},
year = {2022},
doi = {10.1016/j.agrformet.2022.109081},
url = {https://www.sciencedirect.com/science/article/pii/S0168192322002696?via%3Dihub},
}

M. Rossini, M. Celesti, G. Bramati, M. Migliavacca, S. Cogliati, U. Rascher, and R. Colombo, "Evaluation of the Spatial Representativeness of In Situ SIF Observations for the Validation of Medium-Resolution Satellite SIF Products," Remote Sensing, vol. 14, iss. 20, 2022. doi:10.3390/rs14205107
[BibTeX] [PDF]

The upcoming Fluorescence Explorer (FLEX) mission will provide sun-induced fluorescence (SIF) products at unprecedented spatial resolution. Thus, accurate calibration and validation (cal/val) of these products are key to guarantee robust SIF estimates for the assessment and quantification of photosynthetic processes. In this study, we address one specific component of the uncertainty budget related to SIF retrieval: the spatial representativeness of in situ SIF observations compared to medium-resolution SIF products (e.g., 300 m pixel size). Here, we propose an approach to evaluate an optimal sampling strategy to characterise the spatial representativeness of in situ SIF observations based on high-spatial-resolution SIF data. This approach was applied for demonstration purposes to two agricultural areas that have been extensively characterized with a HyPlant airborne imaging spectrometer in recent years. First, we determined the spatial representativeness of an increasing number of sampling points with respect to a reference area (either monocultural crop fields or hypothetical FLEX pixels characterised by different land cover types). Then, we compared different sampling approaches to determine which strategy provided the most representative reference data for a given area. Results show that between 3 and 13.5 sampling points are needed to characterise the average SIF value of both monocultural fields and hypothetical FLEX pixels of the agricultural areas considered in this study. The number of sampling points tends to increase with the standard deviation of SIF of the reference area, as well as with the number of land cover classes in a FLEX pixel, even if the increase is not always statistically significant. This study contributes to guiding cal/val activities for the upcoming FLEX mission, providing useful insights for the selection of the validation site network and particularly for the definition of the best sampling scheme for each site.

@Article{rs14205107,
AUTHOR = {Rossini, Micol and Celesti, Marco and Bramati, Gabriele and Migliavacca, Mirco and Cogliati, Sergio and Rascher, Uwe and Colombo, Roberto},
TITLE = {Evaluation of the Spatial Representativeness of In Situ SIF Observations for the Validation of Medium-Resolution Satellite SIF Products},
JOURNAL = {Remote Sensing},
VOLUME = {14},
YEAR = {2022},
NUMBER = {20},
ARTICLE-NUMBER = {5107},
URL = {https://www.mdpi.com/2072-4292/14/20/5107},
ISSN = {2072-4292},
ABSTRACT = {The upcoming Fluorescence Explorer (FLEX) mission will provide sun-induced fluorescence (SIF) products at unprecedented spatial resolution. Thus, accurate calibration and validation (cal/val) of these products are key to guarantee robust SIF estimates for the assessment and quantification of photosynthetic processes. In this study, we address one specific component of the uncertainty budget related to SIF retrieval: the spatial representativeness of in situ SIF observations compared to medium-resolution SIF products (e.g., 300 m pixel size). Here, we propose an approach to evaluate an optimal sampling strategy to characterise the spatial representativeness of in situ SIF observations based on high-spatial-resolution SIF data. This approach was applied for demonstration purposes to two agricultural areas that have been extensively characterized with a HyPlant airborne imaging spectrometer in recent years. First, we determined the spatial representativeness of an increasing number of sampling points with respect to a reference area (either monocultural crop fields or hypothetical FLEX pixels characterised by different land cover types). Then, we compared different sampling approaches to determine which strategy provided the most representative reference data for a given area. Results show that between 3 and 13.5 sampling points are needed to characterise the average SIF value of both monocultural fields and hypothetical FLEX pixels of the agricultural areas considered in this study. The number of sampling points tends to increase with the standard deviation of SIF of the reference area, as well as with the number of land cover classes in a FLEX pixel, even if the increase is not always statistically significant. This study contributes to guiding cal/val activities for the upcoming FLEX mission, providing useful insights for the selection of the validation site network and particularly for the definition of the best sampling scheme for each site.},
DOI = {10.3390/rs14205107}
}

D. Schulz and J. Börner, "Innovation context and technology traits explain heterogeneity across studies of agricultural technology adoption: A meta‐analysis," Journal of Agricultural Economics, p. 1477–9552.12521, 2022. doi:10.1111/1477-9552.12521
[BibTeX] [PDF] [Video]

@article{schulz_innovation_2022,
title = {Innovation context and technology traits explain heterogeneity across studies of agricultural technology adoption: {A} meta‐analysis},
copyright = {All rights reserved},
issn = {0021-857X, 1477-9552},
shorttitle = {Innovation context and technology traits explain heterogeneity across studies of agricultural technology adoption},
videourl = {https://www.youtube.com/watch?v=x864e3nRxKA},
url = {https://onlinelibrary.wiley.com/doi/10.1111/1477-9552.12521},
doi = {10.1111/1477-9552.12521},
language = {en},
urldate = {2022-11-23},
journal = {Journal of Agricultural Economics},
author = {Schulz, Dario and Börner, Jan},
month = nov,
year = {2022},
pages = {1477--9552.12521},
}

J. Kierdorf, L. V. Junker-Frohn, M. Delaney, D. M. Olave, A. Burkart, H. Jaenicke, O. Muller, U. Rascher, and R. Roscher, "GrowliFlower: An image time-series dataset for GROWth analysis of cauLIFLOWER," Journal of Field Robotics, 2022. doi:10.1002/rob.22122
[BibTeX] [PDF] [Video]

@article{Kierdorf_JournalofFieldRobotics,
title = {GrowliFlower: An image time-series dataset for GROWth analysis of cauLIFLOWER},
journal = {Journal of Field Robotics},
year = {2022},
issn = {1556-4959},
doi = {10.1002/rob.22122},
videourl = {https://www.youtube.com/watch?v=C7A4Bic_7fs
},
url={https://onlinelibrary.wiley.com/doi/pdf/10.1002/rob.22122},
author = {J. Kierdorf and L.V. Junker-Frohn and M. Delaney and M. Donoso Olave and A. Burkart, and H. Jaenicke and O. Muller and U. Rascher and R. Roscher}
}

G. Hölzl, R. B. Rezaeva, J. Kumlehn, and P. Dörmann, "Ablation of glucosinolate accumulation in the oil crop Camelina sativa by targeted mutagenesis of genes encoding the transporters GTR1 and GTR2 and regulators of biosynthesis MYB28 and MYB29," Plant Biotechnology Journal, 2022. doi:10.1111/pbi.13936
[BibTeX] [PDF]

@Article{Doermann_plantbiotechnology,
author = {G. Hölzl and B. Ruzimurodovna Rezaeva and J. Kumlehn and P. Dörmann},
title = {Ablation of glucosinolate accumulation in the oil crop Camelina sativa by targeted mutagenesis of genes encoding the transporters GTR1 and GTR2 and regulators of biosynthesis MYB28 and MYB29},
journal = {Plant Biotechnology Journal},
year = {2022},
doi = {10.1111/pbi.13936},
url = {https://onlinelibrary.wiley.com/doi/full/10.1111/pbi.13936}
}

J. S. Bates, F. Jonard, H. Vereecken, and C. Montzka, "UAS LiDAR Local Maximum Filtering for Individual Maize Detection," in IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium , 2022, pp. 520-522. doi:10.1109/IGARSS46834.2022.9883527
[BibTeX] [PDF]

@INPROCEEDINGS{9883527,
author={Bates, Jordan Steven and Jonard, François and Vereecken, Harry and Montzka, Carsten},
booktitle={IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium},
title={UAS LiDAR Local Maximum Filtering for Individual Maize Detection},
year={2022},
url={https://orbi.uliege.be/bitstream/2268/311694/1/Bates%20IGARSS%202022a.pdf},
pages={520-522},
doi={10.1109/IGARSS46834.2022.9883527}}

J. S. Bates, F. Jonard, R. Bajracharya, H. Vereecken, and C. Montzka, "UAS Lidar Derived Metrics for Winter Wheat Biomass Estimations using Multiple Linear Regression," in IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium , 2022, pp. 7234-7237. doi:10.1109/IGARSS46834.2022.9883339
[BibTeX] [PDF]

@INPROCEEDINGS{9883339,
author={Bates, Jordan Steven and Jonard, François and Bajracharya, Rajina and Vereecken, Harry and Montzka, Carsten},
booktitle={IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium},
title={UAS Lidar Derived Metrics for Winter Wheat Biomass Estimations using Multiple Linear Regression},
year={2022},
url={https://orbi.uliege.be/bitstream/2268/311693/1/Bates%20IGARSS%202022b.pdf
},
pages={7234-7237},
doi={10.1109/IGARSS46834.2022.9883339}}

E. Chakhvashvili, J. Bendig, B. Siegmann, O. Muller, J. Verrelst, and U. Rascher, "LAI and Leaf Chlorophyll Content Retrieval Under Changing Spatial Scale Using a UAV-Mounted Multispectral Camera," in IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium , 2022, pp. 7891-7894. doi:10.1109/IGARSS46834.2022.9883446
[BibTeX]

@INPROCEEDINGS{9883446,
author={Chakhvashvili, Erekle and Bendig, Juliane and Siegmann, Bastian and Muller, Onno and Verrelst, Jochem and Rascher, Uwe},
booktitle={IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium},
title={LAI and Leaf Chlorophyll Content Retrieval Under Changing Spatial Scale Using a UAV-Mounted Multispectral Camera},
year={2022},
volume={},
number={},
pages={7891-7894},
doi={10.1109/IGARSS46834.2022.9883446}}

C. Smitt, M. Halstead, A. Ahmadi, and C. McCool, "Explicitly Incorporating Spatial Information to Recurrent Networks for Agriculture," IEEE Robotics and Automation Letters, vol. 7, iss. 4, pp. 10017-10024, 2022. doi:10.1109/LRA.2022.3188105
[BibTeX] [PDF] [Video]

@ARTICLE{9813583,
author={Smitt, Claus and Halstead, Michael and Ahmadi, Alireza and McCool, Chris},
journal={IEEE Robotics and Automation Letters},
title={Explicitly Incorporating Spatial Information to Recurrent Networks for Agriculture},
year={2022},
url={https://arxiv.org/pdf/2206.13406.pdf},
volume={7},
number={4},
pages={10017-10024},
videourl={https://www.youtube.com/watch?v=1CKHx6xRSA0},
doi={10.1109/LRA.2022.3188105}}

A. Ahmadi, M. Halstead, and C. McCool, "Towards Autonomous Visual Navigation in Arable Fields," in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2022, pp. 6585-6592. doi:10.1109/IROS47612.2022.9981299
[BibTeX] [PDF] [Video]

@INPROCEEDINGS{9981299,
author={Ahmadi, Alireza and Halstead, Michael and McCool, Chris},
booktitle={2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
title={Towards Autonomous Visual Navigation in Arable Fields},
year={2022},
volume={},
number={},
videourl={https://www.youtube.com/watch?v=z2Cb2FFZ2aU},
url={https://arxiv.org/pdf/2109.11936.pdf},
pages={6585-6592},
doi={10.1109/IROS47612.2022.9981299}}

A. Ahmadi, M. Halstead, and C. McCool, "BonnBot-I: A Precise Weed Management and Crop Monitoring Platform," in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2022, pp. 9202-9209. doi:10.1109/IROS47612.2022.9981304
[BibTeX] [PDF]

@INPROCEEDINGS{9981304,
author={Ahmadi, Alireza and Halstead, Michael and McCool, Chris},
booktitle={2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
title={BonnBot-I: A Precise Weed Management and Crop Monitoring Platform},
year={2022},
volume={},
url={https://arxiv.org/pdf/2307.12588v1},
number={},
pages={9202-9209},
doi={10.1109/IROS47612.2022.9981304}}

H. Vereecken, W. Amelung, S. L. Bauke, H. Bogena, N. Brüggemann, C. Montzka, J. Vanderborght, M. Bechtold, G. Blöschl, A. Carminati, M. Javaux, A. G. Konings, J. Kusche, I. Neuweiler, D. Or, S. Steele-Dunne, A. Verhoef, M. Young, and Y. Zhang, "Soil hydrology in the Earthsystem," Nature Reviews Earth & Environment, vol. 3, pp. 573-587, 2022. doi:10.1038/s43017-022-00324-6
[BibTeX] [PDF]

@article{Vereecken2022NatRevEarthEnviron,
author = {Vereecken, Harry AND Amelung, Wulf AND Bauke, Sara L. AND Bogena, Heye AND Brüggemann, Nicolas AND Montzka, Carsten AND Vanderborght, Jan AND Bechtold, Michel AND Blöschl, Günter AND Carminati, Andrea AND Javaux, Mathieu AND Konings, Alexandra G. AND Kusche, Jürgen AND Neuweiler, Insa AND Or, Dani AND Steele-Dunne, Susan AND Verhoef, Anne AND Young, Michael AND Zhang, Yonggen},
title = {{Soil hydrology in the Earthsystem}},
journal = {Nature Reviews Earth & Environment},
volume = {3},
year = {2022},
url={https://centaur.reading.ac.uk/105378/1/Vereecken_Review_Post_Review_finalno_track.pdf},
doi = {10.1038/s43017-022-00324-6},
pages = {573-587},
}

C. W. Kuppe, A. Schnepf, E. Lieres, M. Watt, and J. A. Postma, "Rhizosphere models: their concepts and application to plant-soil ecosystems," Plant and Soil, vol. 474, pp. 17-55, 2022. doi:10.1007/s11104-021-05201-7
[BibTeX] [PDF]

@article{Kuppe2022PlantAndSoil,
author = {Kuppe, C.W. AND Schnepf, A. AND Lieres, E. AND Watt, M. AND Postma, J.A.},
title = {{Rhizosphere models: their concepts and application to plant-soil ecosystems}},
journal = {Plant and Soil},
volume = {474},
year = {2022},
url={https://link.springer.com/article/10.1007/s11104-021-05201-7},
doi = {10.1007/s11104-021-05201-7},
pages = {17-55},
}

L. Jin, J. Rückin, S. H. Kiss, T. Vidal-Calleja, and M. Popović, "Adaptive-Resolution Field Mapping Using Gaussian Process Fusion With Integral Kernels," IEEE Robotics and Automation Letters, vol. 7, iss. 3, pp. 7471-7478, 2022.
[BibTeX] [PDF] [Video]

@ARTICLE{9797797,
author={Jin, Liren and Rückin, Julius and Kiss, Stefan H. and Vidal-Calleja, Teresa and Popović, Marija},
journal={IEEE Robotics and Automation Letters},
title={Adaptive-Resolution Field Mapping Using Gaussian Process Fusion With Integral Kernels},
year={2022},
url={https://arxiv.org/pdf/2109.14257.pdf},
volume={7},
number={3},
pages={7471-7478},
videourl={https://www.youtube.com/watch?v=oJlOsgnNgm}
doi={10.1109/LRA.2022.3183797}}

A. Massfeller, M. Meraner, S. Huettel, and R. Uehleke, "Farmers' acceptance of results-based agri-environmental schemes: A German perspective," Land Use Policy, vol. 120, 2022. doi:10.1016/j.landusepol.2022.106281
[BibTeX] [PDF] [Video]

@article{Massfeller2022LandUsePolicy,
author = {Massfeller, A. AND Meraner, M. AND Huettel, S. AND Uehleke, R.},
title = {{Farmers' acceptance of results-based agri-environmental schemes: A German perspective}},
journal = {Land Use Policy},
volume = {120},
year = {2022},
doi = {10.1016/j.landusepol.2022.106281},
issn = {1-12},
videourl = {https://www.youtube.com/watch?v=4x7mmEmE1-A},
url = {https://www.sciencedirect.com/science/article/pii/S0264837722003088?via%3Dihub},
}

J. S. Bates, F. Jonard, R. Bajracharya, H. Vereecken, and C. Montzka, "Machine Learning with UAS LiDAR for Winter Wheat Biomass Estimations," AGILE GISience Series, vol. 3, pp. 1-4, 2022. doi:10.5194/agile-giss-3-23-2022
[BibTeX] [PDF]

@article{Bates2022AGILEJ,
author = {Bates, Jordan Steven AND Jonard, F. AND Bajracharya, R. AND Vereecken, H. AND Montzka, C.},
title = {{Machine Learning with UAS LiDAR for Winter Wheat Biomass Estimations}},
journal = {AGILE GISience Series},
volume = {3},
issue = {23},
year = {2022},
doi = {10.5194/agile-giss-3-23-2022},
pages = {1-4},
url = {https://agile-giss.copernicus.org/articles/3/23/2022/agile-giss-3-23-2022.pdf},
}

J. Bindics, K. Mamoona, S. Uhse, B. Kogelmann, L. Baggely, D. Reumann, K. D. Ingole, A. Stirnberg, A. Rybecky, M. Darino, F. Navarrete, G. Dochlemann, and A. Djamei, "Many ways to TOPLESS-manipulation of plant auxin signalling by a cluster of fungal effectors," New Phytologist Foundation, 2022. doi:10.1111/nph.18315
[BibTeX] [PDF] [Video]

@article{Djamei2022NewPhytologistFoundation,
author = {Bindics, J. AND Mamoona, K. AND Uhse, S. AND Kogelmann, B. AND Baggely, L. AND Reumann, D. AND Ingole, K. D. AND Stirnberg, A. AND Rybecky, A. AND Darino, M. AND Navarrete, F. AND Dochlemann, G. AND Djamei, A. },
title = {{Many ways to TOPLESS-manipulation of plant auxin signalling by a cluster of fungal effectors}},
journal = {New Phytologist Foundation},
videourl = {https://www.youtube.com/watch?v=wBEWDYpbdZg
},
url={https://nph.onlinelibrary.wiley.com/doi/10.1111/nph.18315},
year = {2022},
doi = {10.1111/nph.18315},
}

C. Latka, A. Parodi, O. van Hal, T. Heckelei, A. Leip, H. Witzke, and H. H. E. van Zanten, "Competing for food waste – Policies’ market feedbacks imply sustainability tradeoffs," Resources, Conservation and Recycling, vol. 186, 2022. doi:10.1016/j.resconrec.2022.106545
[BibTeX] [PDF]

@article{Heckelei2022ResourcesConservationAndRecycling,
author = {Latka, C. AND Parodi, A. AND van Hal, O. AND Heckelei, T. AND Leip, A. AND Witzke, HP. AND van Zanten, H.H.E.},
title = {{Competing for food waste – Policies’ market feedbacks imply sustainability tradeoffs}},
journal = {Resources, Conservation and Recycling},
volume = {186},
year = {2022},
doi = {10.1016/j.resconrec.2022.106545},
url = {https://www.sciencedirect.com/science/article/pii/S0921344922003810},
}

E. Riemer, N. Jyothi Pullagurla, R. Yadav, P. Rana, H. J. Jessen, M. Kamleitner, G. Schaaf, and D. Laha, "Regulation of plant biotic interactions and abiotic stress responses by inositol polyphosphates," Frontiers in Plant Science, vol. 13, pp. 1-18, 2022. doi:10.3389/fpls.2022.944515
[BibTeX] [PDF]

@article{Schaaf2022FrontPlantSci,
author = {Riemer, E. AND Jyothi Pullagurla, N. AND Yadav, R. AND Rana, P. AND Jessen, H. J. AND Kamleitner, M. AND Schaaf, G. AND Laha, D.},
title = {{Regulation of plant biotic interactions and abiotic stress responses by inositol polyphosphates}},
journal = {Frontiers in Plant Science},
volume = {13},
year = {2022},
doi = {10.3389/fpls.2022.944515},
pages = {1-18},
url = {https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2022.944515/full },
}

I. M. Hernandez-Ochoa, T. Gaiser, K. Kersebaum, H. Webber, S. J. Seidel, K. Grahmann, and F. Ewert, "Model-based design of crop diversification through new field arrangements in spatially heterogeneous landscapes. A review.," Agronomy for Sustainable Development, vol. 42, pp. 1-25, 2022. doi:10.1007/s13593-022-00805-4
[BibTeX] [PDF]

@article{Hernandez-Ochoa2022ASD,
author = {Hernandez-Ochoa, I. M. AND Gaiser, T. AND Kersebaum, KC. AND Webber, H. AND Seidel, S. J. AND Grahmann, K. AND Ewert, F. },
title = {{Model-based design of crop diversification through new field arrangements in spatially heterogeneous landscapes. A review.}},
journal = {Agronomy for Sustainable Development},
volume = {42},
issue = {4},
year = {2022},
doi = {10.1007/s13593-022-00805-4},
pages = {1-25},
url = {https://link.springer.com/article/10.1007/s13593-022-00805-4},
}

O. Esmaeelipoor Jahromi, M. Knott, R. K. Janakiram, R. Rahim, and E. Kroener, "Pore-scale simulation of mucilage drainage," Vadose Zone Journal, vol. e20218, pp. 1-13, 2022. doi:10.1002/vzj2.20218
[BibTeX] [PDF] [Video]

@article{EsmaeelipoorJahromi2022VadoseZoneJ,
author = {Esmaeelipoor Jahromi, O. AND Knott, M. AND Janakiram, R. K. AND Rahim, R. AND Kroener, E.},
title = {{Pore-scale simulation of mucilage drainage}},
journal = {Vadose Zone Journal},
volume = {e20218},
year = {2022},
doi = {10.1002/vzj2.20218},
pages = {1-13},
videourl = {https://www.youtube.com/watch?v=UTghZs2235A
},
url = {https://acsess.onlinelibrary.wiley.com/doi/full/10.1002/vzj2.20218},
}

J. Knechtel, L. Klingbeil, J. -H. Haunert, and Y. Dehbi, "Optimal Position and Path Planning for Stop-and-Go Laserscanning for the Acquisition of 3D Building Models." 2022, p. 129–136. doi:10.5194/isprs-annals-V-4-2022-129-2022
[BibTeX] [PDF]

@InProceedings{Knechtel2022ISPRS,
AUTHOR = {Knechtel, J. and Klingbeil, L. and Haunert, J.-H. and Dehbi, Y.},
TITLE = {Optimal Position and Path Planning for Stop-and-Go Laserscanning for the Acquisition of 3D Building Models},
JOURNAL = {ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
VOLUME = {V-4-2022},
YEAR = {2022},
PAGES = {129--136},
URL = {https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-4-2022/129/2022/},
DOI = {10.5194/isprs-annals-V-4-2022-129-2022}
}

L. Klingbeil, A. Dreier, F. Esser, L. Zabawa, D. Pavlic, and H. Kuhlmann, "Mobile Mapping auf dem Acker - hochaufgelöste 3D-Vermessung für nachhaltige Planzenproduktion," Allgemeine Vermessungs-Nachrichten (AVN), vol. 03/2022, pp. 96-103, 2022.
[BibTeX] [PDF]

@article{Klingbeil2022AVN,
author = {Klingbeil, L. AND Dreier, A. AND Esser, F. AND Zabawa, L. AND Pavlic, D. AND Kuhlmann, H.},
title = {Mobile Mapping auf dem Acker - hochaufgelöste 3D-Vermessung für nachhaltige Planzenproduktion},
journal = {Allgemeine Vermessungs-Nachrichten (AVN)},
volume = {03/2022},
issue = {129},
year = {2022},
pages = {96-103},
url = {https://gispoint.de/index.php?eID=dumpFile&t=f&f=12106&token=72624217660d2d72bf505f5e3137f25a3608c4d8&download=},
}

M. Günder, F. R. Ispizua Yamati, J. Kierdorf, R. Roscher, A. -K. Mahlein, and C. Bauckhage, "Agricultural plant cataloging and establishment of a data framework from UAV-based crop images by computer vision," GigaScience, vol. 11, p. 1–14, 2022. doi:10.1093/gigascience/giac054
[BibTeX] [PDF]

@article{guender2022gigascience,
author = {Günder, M. AND Ispizua Yamati, F.R. AND Kierdorf, J. AND Roscher, R. AND Mahlein, A.-K. AND Bauckhage, C.},
title = {{Agricultural plant cataloging and establishment of a data framework from UAV-based crop images by computer vision}},
journal = {GigaScience},
volume = {11},
pages = {1--14},
url={https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giac054/6610009},
year = {2022},
month = {06},
publisher = {Oxford University Press},
doi = {10.1093/gigascience/giac054},
}

S. De Canniere, H. Vereecken, P. Defourny, and F. Jonard, "Remote Sensing of Instantaneous Drought Stress at Canopy Level Using Sun-Induced Chlorophyll Fluorescence and Canopy Reflectance," Remote Sensing, vol. 14, 2022. doi:10.3390/rs14112642
[BibTeX] [PDF]

@Article{decanniere2022remotese,
AUTHOR = {De Canniere, S. AND Vereecken, H. AND Defourny, P. AND Jonard, F.},
TITLE = {Remote Sensing of Instantaneous Drought Stress at Canopy Level Using Sun-Induced Chlorophyll Fluorescence and Canopy Reflectance},
JOURNAL = {Remote Sensing},
VOLUME = {14},
ISSUE = {2072-4292},
YEAR = {2022},
URL = {https://www.mdpi.com/2072-4292/14/11/2642},
DOI = {10.3390/rs14112642}
}

P. Gaugler, R. Schneider, G. Liu, D. Qiu, J. Weber, J. Schmid, N. Jork, M. Häner, K. Ritter, N. Fernández-Rebollo, R. F. H. Giehl, M. N. Trung, R. Yadav, D. Fiedler, V. Gaugler, H. J. Jessen, G. Schaaf, and D. Laha, "Arabidopsis PFA-DSP-Type Phosphohydrolases Target Specific Inositol Pyrophosphate Messengers," Biochemistry, 2022. doi:10.1021/acs.biochem.2c00145
[BibTeX] [PDF]

@article{gaugler2022biochem,
author = {Gaugler, P. AND Schneider, R. AND Liu, G. AND Qiu, D. AND Weber, J. AND Schmid, J. AND Jork, N. AND Häner, M. AND Ritter, K. AND Fernández-Rebollo, N. AND Giehl, R. F. H. AND Trung, M. N. AND Yadav, R. AND Fiedler, D. AND Gaugler, V. AND Jessen, H. J. AND Schaaf, G. AND Laha, D.},
title = {Arabidopsis PFA-DSP-Type Phosphohydrolases Target Specific Inositol Pyrophosphate Messengers},
journal = {Biochemistry},
year = {2022},
doi = {10.1021/acs.biochem.2c00145},
URL = { https://doi.org/10.1021/acs.biochem.2c00145},
}

M. Miranda, L. Zabawa, A. Kicherer, L. Strothmann, U. Rascher, and R. Roscher, "Detection of Anomalous Grapevine Berries Using Variational Autoencoders," Frontiers in Plant Science, vol. 13, 2022. doi:10.3389/fpls.2022.729097
[BibTeX] [PDF]

@article{miranda2022frontplantsci,
author={Miranda, M. AND Zabawa, L. AND Kicherer, A. AND Strothmann, L. AND Rascher, U. AND Roscher, R.},
title={Detection of Anomalous Grapevine Berries Using Variational Autoencoders},
journal={Frontiers in Plant Science},
volume={13},
year={2022},
URL={https://www.frontiersin.org/article/10.3389/fpls.2022.729097},
doi={10.3389/fpls.2022.729097},
}

F. Bauer, L. Lärm, S. Morandage, G. Lobet, J. Vanderborght, H. Vereecken, and A. Schnepf, "Development and Validation of a Deep Learning Based Automated Minirhizotron Image Analysis Pipeline," Plant Phenomics, vol. 2022, 2022. doi:10.34133/2022/9758532
[BibTeX] [PDF]

@article{bauer2022plantpheno,
author = {Bauer, F. AND Lärm, L. AND Morandage, S. AND Lobet, G. AND Vanderborght, J. AND Vereecken, H. AND Schnepf, A.},
title = {{Development and Validation of a Deep Learning Based Automated Minirhizotron Image Analysis Pipeline}},
journal = {Plant Phenomics},
url={https://www.researchgate.net/publication/360930600_Development_and_Validation_of_a_Deep_Learning_Based_Automated_Minirhizotron_Image_Analysis_Pipeline},
volume = {2022},
year = {2022},
doi = {10.34133/2022/9758532},
}

M. Tazifor, E. Zimmermann, J. A. Huisman, M. Dick, A. Mester, and S. Van Waasen, "Model-Based Correction of Temperature-Dependent Measurement Errors in Frequency Domain Electromagnetic Induction (FDEMI) Systems," Sensors, vol. 22, 2022. doi:10.3390/s22103882
[BibTeX] [PDF]

@article{tazifor2022sensors,
author = {Tazifor, M. AND Zimmermann, E. AND Huisman, J.A. AND Dick, M. AND Mester, A. AND Van Waasen, S.},
title = {{Model-Based Correction of Temperature-Dependent Measurement Errors in Frequency Domain Electromagnetic Induction (FDEMI) Systems}},
journal = {Sensors},
url={https://www.mdpi.com/1424-8220/22/10/3882},
volume = {22},
year = {2022},
doi = {10.3390/s22103882},
}

I. Saado, K. Chia, R. Betz, A. Alcantara, A. Pettko-Szandtner, F. Navarrete, J. C. D'Auria, M. V. Kolomiets, M. Melzer, I. Feussner, and A. Djamei, "Effector-mediated relocalization of a maize lipoxygenase protein triggers susceptibility to Ustilago maydis," The Plant Cell, pp. 1-21, 2022. doi:10.1093/plcell/koac105
[BibTeX] [PDF]

@article{saado2022plantcell,
author = {Saado, I. AND Chia, K. AND Betz, R. AND Alcantara, A. AND Pettko-Szandtner, A. AND Navarrete, F. AND D'Auria, J. C. AND Kolomiets, M. V. AND Melzer, M. AND Feussner, I. AND Djamei, A.},
title = {{Effector-mediated relocalization of a maize lipoxygenase
protein triggers susceptibility to Ustilago maydis}},
journal = {The Plant Cell},
year = {2022},
issn = {1040-4651},
pages = {1-21},
doi = {10.1093/plcell/koac105},
url = {https://academic.oup.com/plcell/advance-article-pdf/doi/10.1093/plcell/koac105/43541391/koac105.pdf},
}

L. Zabawa, A. Kicherer, L. Klingbeil, R. Töpfer, R. Roscher, and H. Kuhlmann, "Image-based analysis of yield parameters in viticulture," Biosystems Engineering, vol. 218, pp. 94-109, 2022. doi:10.1016/j.biosystemseng.2022.04.009
[BibTeX] [PDF]

Yield estimation is of great interest in viticulture, since an early estimation could influence management decisions of winegrowers. The current practice involves destructive sampling of small sets in the field and a subsequent detailed analysis in the laboratory. The results are extrapolated to the field and only approximate the actual conditions. Therefore, research in recent years focused on sensor-based systems mounted on field vehicles since they offer a fast, accurate and robust data acquisition. However many works stop after detecting fruits, rarely the actual yield estimation is tackled. We present a novel yield estimation pipeline that uses images captured by a multi-camera system. The system is mounted on a field phenotyping platform called Phenoliner, which has been built from a modified grapevine harvester. We use a neural network whose output is used to count berries in single images. In contrast to other existing methods we take the step from the single vine image processing to the plant level. The information of multiple images is used to acquire a count on plant level and the approach is extended to the processing based on the whole row. The acquired berry counts are used as input for the yield estimation, and we explore the limitations and potentials of our pipeline. We identify the variability of the leaf occlusion as the main limiting factor, but nonetheless we achieve a mean absolute yield prediction error of 26\% for plants in the vertical shoot positioned system. We evaluate each described stage comprehensively in this study.

@article{ZABAWA202294,
title = {Image-based analysis of yield parameters in viticulture},
journal = {Biosystems Engineering},
volume = {218},
pages = {94-109},
year = {2022},
issn = {1537-5110},
doi = {10.1016/j.biosystemseng.2022.04.009},
url = {https://www.phenorob.de/wp-content/uploads/2024/08/Zabawa_ImageBasedAnalysisYieldParameters_NoComment.pdf},
author = {Zabawa, L. AND Kicherer, A. AND Klingbeil, L. AND Töpfer, R. AND Roscher, R. AND Kuhlmann, H.},
keywords = {Deep Learning, Semantic Segmentation, Geoinformation, Viticulture, Yield Estimation},
abstract = {Yield estimation is of great interest in viticulture, since an early estimation could influence management decisions of winegrowers. The current practice involves destructive sampling of small sets in the field and a subsequent detailed analysis in the laboratory. The results are extrapolated to the field and only approximate the actual conditions. Therefore, research in recent years focused on sensor-based systems mounted on field vehicles since they offer a fast, accurate and robust data acquisition. However many works stop after detecting fruits, rarely the actual yield estimation is tackled. We present a novel yield estimation pipeline that uses images captured by a multi-camera system. The system is mounted on a field phenotyping platform called Phenoliner, which has been built from a modified grapevine harvester. We use a neural network whose output is used to count berries in single images. In contrast to other existing methods we take the step from the single vine image processing to the plant level. The information of multiple images is used to acquire a count on plant level and the approach is extended to the processing based on the whole row. The acquired berry counts are used as input for the yield estimation, and we explore the limitations and potentials of our pipeline. We identify the variability of the leaf occlusion as the main limiting factor, but nonetheless we achieve a mean absolute yield prediction error of 26\% for plants in the vertical shoot positioned system. We evaluate each described stage comprehensively in this study.}
}

A. S. Wendel, S. L. Bauke, W. Amelung, and C. Knief, "Root-rhizosphere-soil interactions in biopores," Plant and Soil, p. 1–25, 2022. doi:10.1007/s11104-022-05406-4
[BibTeX] [PDF]

@article{wendel2022root,
author = {Wendel, A.S. AND Bauke, S.L. AND Amelung, W. AND Knief, C.},
title = {{Root-rhizosphere-soil interactions in biopores}},
journal = {Plant and Soil},
year = {2022},
doi = {10.1007/s11104-022-05406-4},
pages = {1--25},
url = {https://link.springer.com/article/10.1007/s11104-022-05406-4},
}

D. Demie, T. Döring, M. Finckh, W. van der Werf, J. Enjalbert, and S. Seidel, "Mixture X Genotype Effects in Cereal/Legume Intercropping," Frontiers in Plant Science, vol. 13, 2022. doi:10.3389/fpls.2022.846720
[BibTeX] [PDF] [Video]

@article{demie2022frontiers,
author = {Demie, D. AND Döring, T. AND Finckh, M. AND van der Werf, W. AND Enjalbert, J. AND Seidel, S.},
title = {{Mixture X Genotype Effects in Cereal/Legume Intercropping}},
journal = {Frontiers in Plant Science},
volume = {13},
issue = {1664-462X},
videourl  = {https://www.youtube.com/watch?v=WEawxTn4kVw}, url={https://www.frontiersin.org/articles/10.3389/fpls.2022.846720/full},
year = {2022},
doi = {10.3389/fpls.2022.846720},
}

E. Chakhvashvili, B. Siegmann, O. Muller, J. Verrelst, J. Bendig, T. Kraska, and U. Rascher, "Retrieval of Crop Variables from Proximal Multispectral UAV Image Data Using PROSAIL in Maize Canopy," Remote Sensing, vol. 14, 2022. doi:10.3390/rs14051247
[BibTeX] [PDF]

@article{Chakhvashvili2022remote,
author = {Chakhvashvili, E. AND Siegmann, B. AND Muller, O. AND Verrelst, J. AND Bendig, J. AND Kraska, T. AND Rascher, U.},
title = {{Retrieval of Crop Variables from Proximal Multispectral UAV Image Data Using PROSAIL in Maize Canopy}},
journal = {Remote Sensing},
url={https://www.mdpi.com/2072-4292/14/5/1247},
volume = {14},
issue = {2072-4292},
year = {2022},
doi = {10.3390/rs14051247},
}

X. Zeng, T. Zaenker, and M. Bennewitz, "Deep Reinforcement Learning for Next-Best-View Planning in Agricultural Applications," in Proc.~of the IEEE International Conference on Robotics & Automation (ICRA) , 2022, pp. 2323-2329. doi:10.1109/ICRA46639.2022.9811800
[BibTeX] [PDF] [Code]

@InProceedings{Zeng22icra,
author = {X. Zeng and T. Zaenker and M. Bennewitz},
title = {Deep Reinforcement Learning for Next-Best-View Planning in Agricultural Applications},
codeurl = {https://github.com/zengxyu/vpp-learning},
url={https://www.hrl.uni-bonn.de/publications/zeng22icra.pdf},
doi={10.1109/ICRA46639.2022.9811800},
pages={2323-2329},
booktitle = {Proc.~of the IEEE International Conference on Robotics \& Automation (ICRA)},
year = {2022}
}

W. Shi, Y. Zhou, X. Zeng, S. Li, and M. Bennewitz, "Enhanced Spatial Attention Graph for Motion Planning in Crowded, Partially Observable Environments," in 2022 International Conference on Robotics and Automation (ICRA) , 2022, pp. 4750-4756. doi:10.1109/ICRA46639.2022.9812322
[BibTeX] [PDF] [Code]

@INPROCEEDINGS{9812322,
author={Shi, Weixian and Zhou, Yanying and Zeng, Xiangyu and Li, Shijie and Bennewitz, Maren},
booktitle={2022 International Conference on Robotics and Automation (ICRA)},
title={Enhanced Spatial Attention Graph for Motion Planning in Crowded, Partially Observable Environments},
year={2022},
volume={},
codeurl={https://github.com/weixians/esa},
url={https://www.hrl.uni-bonn.de/publications/shi22icra.pdf},
number={},
pages={4750-4756},
doi={10.1109/ICRA46639.2022.9812322}}

D. Khare, T. Selzner, D. Leitner, J. Vanderborght, H. Vereecken, and A. Schnepf, "Root System Scale Models Significantly Overestimate Root Water Uptake at Drying Soil Conditions," Frontiers in Plant Science, vol. 13, 2022. doi:10.3389/fpls.2022.798741
[BibTeX] [PDF]

Soil hydraulic conductivity (k_soil) drops significantly in dry soils, resulting in steep soil water potential gradients (ψ_s) near plant roots during water uptake. Coarse soil grid resolutions in root system scale (RSS) models of root water uptake (RWU) generally do not spatially resolve this gradient in drying soils which can lead to a large overestimation of RWU. To quantify this, we consider a benchmark scenario of RWU from drying soil for which a numerical reference solution is available. We analyze this problem using a finite volume scheme and investigate the impact of grid size on the RSS model results. At dry conditions, the cumulative RWU was overestimated by up to 300\% for the coarsest soil grid of 4.0 cm and by 30\% for the finest soil grid of 0.2 cm, while the computational demand increased from 19 s to 21 h. As an accurate and computationally efficient alternative to the RSS model, we implemented a continuum multi-scale model where we keep a coarse grid resolution for the bulk soil, but in addition, we solve a 1-dimensional radially symmetric soil model at rhizosphere scale around individual root segments. The models at the two scales are coupled in a mass-conservative way. The multi-scale model compares best to the reference solution (−20\%) at much lower computational costs of 4 min. Our results demonstrate the need to shift to improved RWU models when simulating dry soil conditions and highlight that results for dry conditions obtained with RSS models of RWU should be interpreted with caution.

@Article{10.3389/fpls.2022.798741,
author = {Khare, Deepanshu and Selzner, Tobias and Leitner, Daniel and Vanderborght, Jan and Vereecken, Harry and Schnepf, Andrea},
title = {Root System Scale Models Significantly Overestimate Root Water Uptake at Drying Soil Conditions},
journal = {Frontiers in Plant Science},
volume = {13},
year = {2022},
url = {https://www.frontiersin.org/article/10.3389/fpls.2022.798741},
doi = {10.3389/fpls.2022.798741},
issn = {1664-462X},
abstract = {Soil hydraulic conductivity (k_soil) drops significantly in dry soils, resulting in steep soil water potential gradients (ψ_s) near plant roots during water uptake. Coarse soil grid resolutions in root system scale (RSS) models of root water uptake (RWU) generally do not spatially resolve this gradient in drying soils which can lead to a large overestimation of RWU. To quantify this, we consider a benchmark scenario of RWU from drying soil for which a numerical reference solution is available. We analyze this problem using a finite volume scheme and investigate the impact of grid size on the RSS model results. At dry conditions, the cumulative RWU was overestimated by up to 300\% for the coarsest soil grid of 4.0 cm and by 30\% for the finest soil grid of 0.2 cm, while the computational demand increased from 19 s to 21 h. As an accurate and computationally efficient alternative to the RSS model, we implemented a continuum multi-scale model where we keep a coarse grid resolution for the bulk soil, but in addition, we solve a 1-dimensional radially symmetric soil model at rhizosphere scale around individual root segments. The models at the two scales are coupled in a mass-conservative way. The multi-scale model compares best to the reference solution (−20\%) at much lower computational costs of 4 min. Our results demonstrate the need to shift to improved RWU models when simulating dry soil conditions and highlight that results for dry conditions obtained with RSS models of RWU should be interpreted with caution.},
}

S. Li, X. Chen, Y. Liu, D. Dai, C. Stachniss, and J. Gall, "Multi-Scale Interaction for Real-Time LiDAR Data Segmentation on an Embedded Platform," IEEE Robotics and Automation Letters, vol. 7, iss. 2, pp. 738-745, 2022. doi:10.1109/LRA.2021.3132059
[BibTeX] [PDF] [Code] [Video]

@Article{9633188,
author = {Li, Shijie and Chen, Xieyuanli and Liu, Yun and Dai, Dengxin and Stachniss, Cyrill and Gall, Juergen},
journal = {IEEE Robotics and Automation Letters},
title = {Multi-Scale Interaction for Real-Time LiDAR Data Segmentation on an Embedded Platform},
year = {2022},
codeurl = {https://github.com/sj-li/MINet},
videourl  = {https://www.youtube.com/watch?v=WDhtz5tZ5vQ},
url={https://arxiv.org/pdf/2008.09162.pdf},
volume = {7},
number = {2},
pages = {738-745},
doi = {10.1109/LRA.2021.3132059},
}

I. Vizzo, T. Guadagnino, J. Behley, and C. Stachniss, "VDBFusion: Flexible and Efficient TSDF Integration of Range Sensor Data," Sensors, vol. 22, iss. 3, 2022. doi:10.3390/s22031296
[BibTeX] [PDF] [Code]

@Article{vizzo2022sensors,
author = {Vizzo, Ignacio and Guadagnino, Tiziano and Behley, Jens and Stachniss, Cyrill},
title = {VDBFusion: Flexible and Efficient TSDF Integration of Range Sensor Data},
journal = {Sensors},
volume = {22},
year = {2022},
number = {3},
article-number= {1296},
codeurl  = {https://github.com/PRBonn/vdbfusion},
url = {https://www.mdpi.com/1424-8220/22/3/1296},
issn = {1424-8220},
doi = {10.3390/s22031296},
}

L. Wiesmann, R. Marcuzzi, C. Stachniss, and J. Behley, "Retriever: Point Cloud Retrieval in Compressed 3D Maps," in 2022 International Conference on Robotics and Automation (ICRA) , 2022, pp. 10925-10932. doi:10.1109/ICRA46639.2022.9811785
[BibTeX] [PDF] [Code] [Video]

@INPROCEEDINGS{9811785,
author={Wiesmann, Louis and Marcuzzi, Rodrigo and Stachniss, Cyrill and Behley, Jens},
booktitle={2022 International Conference on Robotics and Automation (ICRA)},
title={Retriever: Point Cloud Retrieval in Compressed 3D Maps},
year={2022},
volume={},
number={},
codeurl={https://github.com/PRBonn/retriever
},
videourl={https://www.youtube.com/watch?v=ZN3_VTzo-KM},
url={https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/wiesmann2022icra.pdf},
pages={10925-10932},
doi={10.1109/ICRA46639.2022.9811785}}

M. Knott, M. Ani, E. Kroener, and D. Diehl, "Effect of changing chemical environment on physical properties of maize root mucilage," Plant Soil, vol. 478, p. 85–101, 2022. doi:10.1007/s11104-022-05577-0
[BibTeX] [PDF]

@article{knott2022effect,
author= {Knott, Mathilde and Ani, Mina and Kroener, Eva and Diehl, Dörte},
title= {Effect of changing chemical environment on physical properties of maize root mucilage},
journal= {Plant Soil},
volume = {478},
year= {2022},
pages = {85–101},
doi = {10.1007/s11104-022-05577-0},
url = {https://link.springer.com/article/10.1007/s11104-022-05577-0},
}

E. Marks, F. Magistri, and C. Stachniss, "Precise 3D Reconstruction of Plants from UAV Imagery Combining Bundle Adjustment and Template Matching," in 2022 International Conference on Robotics and Automation (ICRA) , 2022, pp. 2259-2265. doi:10.1109/ICRA46639.2022.9811358
[BibTeX] [PDF] [Video]

@INPROCEEDINGS{9811358,
author={Marks, Elias and Magistri, Federico and Stachniss, Cyrill},
booktitle={2022 International Conference on Robotics and Automation (ICRA)},
title={Precise 3D Reconstruction of Plants from UAV Imagery Combining Bundle Adjustment and Template Matching},
year={2022},
volume={},
number={},
videourl={https://www.youtube.com/watch?v=kSVEF8CW2J0},
url={https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/marks2022icra.pdf},
pages={2259-2265},
doi={10.1109/ICRA46639.2022.9811358}}

L. Mau, S. Junker, H. Bochmann, Y. E. Mihiret, J. M. Kelm, S. D. Schrey, U. Roessner, G. Schaaf, M. Watt, J. Kant, and B. Arsova, "Root Growth and Architecture of Wheat and Brachypodium Vary in Response to Algal Fertilizer in Soil and Solution," Agronomy, vol. 12, iss. 2, 2022. doi:10.3390/agronomy12020285
[BibTeX] [PDF]

Alternative, recycled sources for mined phosphorus (P) fertilizers are needed to sustain future crop growth. Quantification of phenotypic adaptations and performance of plants with a recycled nutrient source is required to identify breeding targets and agronomy practices for new fertilization strategies. In this study, we tested the phenotypic responses of wheat (Triticum aestivum) and its genetic model, Brachypodium (Brachypodium distachyon), to dried algal biomass (with algae or high or low mineral P) under three growing conditions (fabricated ecosystems (EcoFABs), hydroponics, and sand). For both species, algal-grown plants had similar shoot biomass to mineral-grown plants, taking up more P than the low mineral P plants. Root phenotypes however were strongly influenced by nutrient form, especially in soilless conditions. Algae promoted the development of shorter and thicker roots, notably first and second order lateral roots. Root hairs were 21\% shorter in Brachypodium, but 24\% longer in wheat with algae compared to mineral high P. Our results are encouraging to new recycled fertilization strategies, showing algae is a nutrient source to wheat and Brachypodium. Variation in root phenotypes showed algal biomass is sensed by roots and is taken up at a higher amount per root length than mineral P. These phenotypes can be selected and further adapted in phenotype-based breeding for future renewal agriculture systems.

@Article{agronomy12020285,
author = {Mau, Lisa and Junker, Simone and Bochmann, Helena and Mihiret, Yeshambel E. and Kelm, Jana M. and Schrey, Silvia D. and Roessner, Ute and Schaaf, Gabriel and Watt, Michelle and Kant, Josefine and Arsova, Borjana},
title = {Root Growth and Architecture of Wheat and Brachypodium Vary in Response to Algal Fertilizer in Soil and Solution},
journal = {Agronomy},
volume = {12},
year = {2022},
number = {2},
article-number= {285},
url = {https://www.mdpi.com/2073-4395/12/2/285},
issn = {2073-4395},
abstract = {Alternative, recycled sources for mined phosphorus (P) fertilizers are needed to sustain future crop growth. Quantification of phenotypic adaptations and performance of plants with a recycled nutrient source is required to identify breeding targets and agronomy practices for new fertilization strategies. In this study, we tested the phenotypic responses of wheat (Triticum aestivum) and its genetic model, Brachypodium (Brachypodium distachyon), to dried algal biomass (with algae or high or low mineral P) under three growing conditions (fabricated ecosystems (EcoFABs), hydroponics, and sand). For both species, algal-grown plants had similar shoot biomass to mineral-grown plants, taking up more P than the low mineral P plants. Root phenotypes however were strongly influenced by nutrient form, especially in soilless conditions. Algae promoted the development of shorter and thicker roots, notably first and second order lateral roots. Root hairs were 21\% shorter in Brachypodium, but 24\% longer in wheat with algae compared to mineral high P. Our results are encouraging to new recycled fertilization strategies, showing algae is a nutrient source to wheat and Brachypodium. Variation in root phenotypes showed algal biomass is sensed by roots and is taken up at a higher amount per root length than mineral P. These phenotypes can be selected and further adapted in phenotype-based breeding for future renewal agriculture systems.},
doi = {10.3390/agronomy12020285},
}

A. Deja-Muylle, D. Opdenacker, B. Parizot, H. Motte, G. Lobet, V. Storme, P. Clauw, M. Njo, and T. Beeckman, "Genetic Variability of Arabidopsis thaliana Mature Root System Architecture and Genome-Wide Association Study," Frontiers in Plant Science, vol. 12, 2022. doi:10.3389/fpls.2021.814110
[BibTeX] [PDF]

@Article{deja2022genetic,
title = {Genetic Variability of Arabidopsis thaliana Mature Root System Architecture and Genome-Wide Association Study},
author = {Deja-Muylle, Agnieszka and Opdenacker, Davy and Parizot, Boris and Motte, Hans and Lobet, Guillaume and Storme, Veronique and Clauw, Pieter and Njo, Maria and Beeckman, Tom},
journal = {Frontiers in Plant Science},
volume = {12},
url={https://www.frontiersin.org/articles/10.3389/fpls.2021.814110/full},
doi={10.3389/fpls.2021.814110},
year = {2022},
}

F. Ispizua Yamati, M. Günder, C. Bauckhage, and A. Mahlein, "Sensing the occurrence and dynamics of Cercospora leaf spot disease using UAV-supported image data and deep learning," Sugar Industry, vol. 147, 2022. doi:10.36961/si28345
[BibTeX] [Video]

@Article{ispizua2022sugar,
title = {Sensing the occurrence and dynamics of Cercospora leaf spot disease using UAV-supported image data and deep learning},
author = {Ispizua Yamati, Facundo and Günder, Maurice and Bauckhage, Christian and Mahlein, Anne-Kathrin},
journal = {Sugar Industry},
volume = {147},
issn = {2},
pages {79-86},
year = {2022},
videourl = {https://www.youtube.com/watch?v=9yUOaVFsfiw},
doi = {10.36961/si28345},
}

B. Mersch, X. Chen, J. Behley, and C. Stachniss, "Self-supervised point cloud prediction using 3d spatio-temporal convolutional networks," in Conference on Robot Learning , 2022, p. 1444–1454. doi:10.48550/arXiv.2110.04076
[BibTeX] [PDF] [Code] [Video]

@InProceedings{mersch2022self,
title = {Self-supervised point cloud prediction using 3d spatio-temporal convolutional networks},
author = {Mersch, Benedikt and Chen, Xieyuanli and Behley, Jens and Stachniss, Cyrill},
booktitle = {Conference on Robot Learning},
pages = {1444--1454},
codeurl  = {https://github.com/PRBonn/point-cloud-prediction},
videourl = {https://www.youtube.com/watch?v=-pSZpPgFAso},
url={https://proceedings.mlr.press/v164/mersch22a/mersch22a.pdf},
doi={10.48550/arXiv.2110.04076},
year = {2022},
organization = {PMLR},
}

J. Weyler, F. Magistri, P. Seitz, J. Behley, and C. Stachniss, "In-Field Phenotyping Based on Crop Leaf and Plant Instance Segmentation," in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2022, p. 2725–2734. doi:10.1109/wacv51458.2022.00302
[BibTeX] [PDF] [Code] [Video]

@InProceedings{weyler2022field,
title = {In-Field Phenotyping Based on Crop Leaf and Plant Instance Segmentation},
author = {Weyler, Jan and Magistri, Federico and Seitz, Peter and Behley, Jens and Stachniss, Cyrill},
codeurl  ={https://github.com/PRBonn/leaf-plant-instance-segmentation
},
videourl = {https://www.youtube.com/watch?v=1meoJt7JZfQ
},
url={https://openaccess.thecvf.com/content/WACV2022/papers/Weyler_In-Field_Phenotyping_Based_on_Crop_Leaf_and_Plant_Instance_Segmentation_WACV_2022_paper.pdf},
doi={10.1109/wacv51458.2022.00302},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
pages = {2725--2734},
year = {2022},
}

M. Morisse, D. M. Wells, E. J. Millet, M. Lillemo, S. Fahrner, F. Cellini, P. Lootens, O. Muller, J. M. Herrera, A. R. Bentley, and M. Janni, "A European perspective on opportunities and demands for field-based crop phenotyping," Field Crops Research, vol. 276, p. 108371, 2022. doi:10.1016/j.fcr.2021.108371
[BibTeX] [PDF]

The challenges of securing future food security will require deployment of innovative technologies to accelerate crop production. Plant phenotyping methods have advanced significantly, spanning low-cost hand-held devices to large-scale satellite imaging. Field-based phenotyping aims to capture plant response to the environment, generating data that can be used to inform breeding and selection requirements. This in turn requires access to multiple representative locations and capacities for collecting useful information. In this paper we identify the current challenges in access to field phenotyping in multiple locations in Europe based on stakeholder feedback. We present a map of current infrastructure and propose opportunities for greater integration of existing facilities for meeting different user requirements. We also review the currently available technology and data requirements for effective multi-location field phenotyping and provide recommendations for ensuring future access and co-ordination. Taken together we provide an overview of the current status of European field phenotyping capabilities and provides a roadmap for their future use to support crop improvement. This provides a wider framework for the analysis and planning of field phenotyping activities for crop improvement worldwide.

@article{MORISSE2022108371,
title = {A European perspective on opportunities and demands for field-based crop phenotyping},
journal = {Field Crops Research},
volume = {276},
pages = {108371},
year = {2022},
issn = {0378-4290},
doi = {10.1016/j.fcr.2021.108371},
url = {https://repository.cimmyt.org/server/api/core/bitstreams/bc7a3289-c716-401d-b4a5-33a2be7b90e8/content},
author = {Merlijn Morisse and Darren M. Wells and Emilie J. Millet and Morten Lillemo and Sven Fahrner and Francesco Cellini and Peter Lootens and Onno Muller and Juan M. Herrera and Alison R. Bentley and Michela Janni},
keywords = {Food security, Phenotyping networks, Remote sensing, Plant breeding},
abstract = {The challenges of securing future food security will require deployment of innovative technologies to accelerate crop production. Plant phenotyping methods have advanced significantly, spanning low-cost hand-held devices to large-scale satellite imaging. Field-based phenotyping aims to capture plant response to the environment, generating data that can be used to inform breeding and selection requirements. This in turn requires access to multiple representative locations and capacities for collecting useful information. In this paper we identify the current challenges in access to field phenotyping in multiple locations in Europe based on stakeholder feedback. We present a map of current infrastructure and propose opportunities for greater integration of existing facilities for meeting different user requirements. We also review the currently available technology and data requirements for effective multi-location field phenotyping and provide recommendations for ensuring future access and co-ordination. Taken together we provide an overview of the current status of European field phenotyping capabilities and provides a roadmap for their future use to support crop improvement. This provides a wider framework for the analysis and planning of field phenotyping activities for crop improvement worldwide.}
}

Z. Ballouch, R. Hajji, and M. Ettarid, "Toward a Deep Learning Approach for Automatic Semantic Segmentation of 3D Lidar Point Clouds in Urban Areas," in Geospatial Intelligence, Springer, 2022, p. 67–77. doi:10.1007/978-3-030-80458-9_6
[BibTeX] [PDF]

@InCollection{ballouch2022toward,
title = {Toward a Deep Learning Approach for Automatic Semantic Segmentation of 3D Lidar Point Clouds in Urban Areas},
author = {Ballouch, Zouhair and Hajji, Rafika and Ettarid, Mohamed},
booktitle = {Geospatial Intelligence},
pages = {67--77},
url={https://orbi.uliege.be/bitstream/2268/290748/1/Toward%20a%20Deep%20Learning%20Approach%20for%20Automatic%20Semantic%20Segmentation%20of%203D%20Lidar%20Point%20Clouds%20in%20Urban%20Areas.pdf},
doi={10.1007/978-3-030-80458-9_6},
year = {2022},
publisher = {Springer},
}

S. Thomas, J. Behmann, U. Rascher, and A. Mahlein, "Evaluation of the benefits of combined reflection and transmission hyperspectral imaging data through disease detection and quantification in plant–pathogen interactions," Journal of Plant Diseases and Protection, p. 1–16, 2022. doi:10.1007/s41348-022-00570-2
[BibTeX] [PDF]

@Article{thomas2022evaluation,
title = {Evaluation of the benefits of combined reflection and transmission hyperspectral imaging data through disease detection and quantification in plant--pathogen interactions},
author = {Thomas, Stefan and Behmann, Jan and Rascher, Uwe and Mahlein, Anne-Katrin},
journal = {Journal of Plant Diseases and Protection},
pages = {1--16},
year = {2022},
doi={10.1007/s41348-022-00570-2},
url={https://www.phenorob.de/wp-content/uploads/2024/08/Thomas_2022_JPlDisProtect_129_505.pdf},
publisher = {Springer},
}

S. Stark, L. Biber-Freudenberger, T. Dietz, N. Escobar, J. J. Förster, J. Henderson, N. Laibach, and J. Börner, "Sustainability implications of transformation pathways for the bioeconomy," Sustainable Production and Consumption, vol. 29, p. 215–227, 2022. doi:10.1016/j.spc.2021.10.011
[BibTeX] [PDF]

@Article{stark2022sustainability,
title = {Sustainability implications of transformation pathways for the bioeconomy},
author = {Stark, Sascha and Biber-Freudenberger, Lisa and Dietz, Thomas and Escobar, Neus and F{\"o}rster, Jan Janosch and Henderson, James and Laibach, Natalie and B{\"o}rner, Jan},
journal = {Sustainable Production and Consumption},
volume = {29},
pages = {215--227},
url={https://www.sciencedirect.com/science/article/pii/S2352550921002943},
doi={10.1016/j.spc.2021.10.011},
year = {2022},
publisher = {Elsevier},
}

M. Weigand, E. Zimmermann, V. Michels, J. A. Huisman, and A. Kemna, "Design and operation of a long-term monitoring system for spectral electrical impedance tomography (sEIT)," Geoscientific Instrumentation, Methods and Data Systems Discussions, p. 1–35, 2022. doi:10.5194/gi-11-413-2022
[BibTeX] [PDF]

@Article{weigand2022design,
title = {Design and operation of a long-term monitoring system for spectral electrical impedance tomography (sEIT)},
author = {Weigand, Maximilian and Zimmermann, Egon and Michels, Valentin and Huisman, Johan Alexander and Kemna, Andreas},
journal = {Geoscientific Instrumentation, Methods and Data Systems Discussions},
pages = {1--35},
year = {2022},
url={https://gi.copernicus.org/articles/11/413/2022/},
doi={10.5194/gi-11-413-2022},
publisher = {Copernicus GmbH},
}

R. A. Rosu and S. Behnke, "NeuralMVS: Bridging Multi-View Stereo and Novel View Synthesis," in Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2022 , 2022. doi:10.1109/ijcnn55064.2022.9892024
[BibTeX] [PDF] [Code]

@inproceedings{rosuneural2022,
author = {Rosu, Radu Alexandru and Behnke, Sven},
year = {2022},
month = {07},
pages = {},
codeurl = {https://github.com/AIS-Bonn/neural_mvs
},
url={https://arxiv.org/pdf/2108.03880.pdf},
doi={10.1109/ijcnn55064.2022.9892024},
title = {NeuralMVS: Bridging Multi-View Stereo and Novel View Synthesis},
booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2022},
}

J. Kierdorf, I. Weber, A. Kicherer, L. Zabawa, L. Drees, and R. Roscher, "Behind the leaves: Estimation of occluded grapevine berries with conditional generative adversarial networks," Frontiers in Artificial Intelligence, 2022. doi:10.3389/frai.2022.830026
[BibTeX] [PDF] [Video]

@Article{kierdorf2022behind,
title = {Behind the leaves: Estimation of occluded grapevine berries with conditional generative adversarial networks},
author = {Kierdorf, Jana and Weber, Immanuel and Kicherer, Anna and Zabawa, Laura and Drees, Lukas and Roscher, Ribana},
videourl  = {https://www.youtube.com/watch?v=vqxJuRDhH8E
}, url={https://www.frontiersin.org/articles/10.3389/frai.2022.830026/full},
doi={10.3389/frai.2022.830026},
journal = {Frontiers in Artificial Intelligence},
year = {2022},
}

A. Schnepf, A. Carminati, M. Ahmed, M. Ani, P. Benard, J. Bentz, M. Bonkowski, M. Brax, D. Diehl, P. Duddek, and others, "Linking rhizosphere processes across scales: Opinion," Plant and Soil, 2022. doi:10.1101/2021.07.08.451655
[BibTeX] [PDF]

@Article{schnepf2021linking,
title = {Linking rhizosphere processes across scales: Opinion},
author = {Schnepf, A and Carminati, A and Ahmed, MA and Ani, M and Benard, P and Bentz, J and Bonkowski, M and Brax, M and Diehl, D and Duddek, P and others},
journal = {Plant and Soil},
url={https://link.springer.com/article/10.1007/s11104-022-05306-7},
doi={10.1101/2021.07.08.451655},
year = {2022},
}

T. LaRue, H. Lindner, A. Srinivas, M. Exposito-Alonso, G. Lobet, and J. R. Dinneny, "Uncovering natural variation in root system architecture and growth dynamics using a robotics-assisted phenomics platform," eLife, vol. 11, p. e76968, 2022. doi:10.7554/eLife.76968
[BibTeX] [PDF]

The plant kingdom contains a stunning array of complex morphologies easily observed above-ground, but more challenging to visualize below-ground. Understanding the magnitude of diversity in root distribution within the soil, termed root system architecture (RSA), is fundamental to determining how this trait contributes to species adaptation in local environments. Roots are the interface between the soil environment and the shoot system and therefore play a key role in anchorage, resource uptake, and stress resilience. Previously, we presented the GLO-Roots (Growth and Luminescence Observatory for Roots) system to study the RSA of soil-grown \textit{Arabidopsis thaliana} plants from germination to maturity (Rellán-Álvarez et al. 2015). In this study, we present the automation of GLO-Roots using robotics and the development of image analysis pipelines in order to examine the temporal dynamic regulation of RSA and the broader natural variation of RSA in Arabidopsis, over time. These datasets describe the developmental dynamics of two independent panels of accessions and reveal highly complex and polygenic RSA traits that show significant correlation with climate variables of the accessions' respective origins.

@Article{larue2021uncovering,
title = {Uncovering natural variation in root system architecture and growth dynamics using a robotics-assisted phenomics platform},
author = {LaRue, Therese and Lindner, Heike and Srinivas, Ankit and Exposito-Alonso, Moises and Lobet, Guillaume and Dinneny, Jos{\'e} R},
volume = 11,
year = 2022,
pages = {e76968},
doi = {10.7554/eLife.76968},
url = {https://doi.org/10.7554/eLife.76968},
abstract = {The plant kingdom contains a stunning array of complex morphologies easily observed above-ground, but more challenging to visualize below-ground. Understanding the magnitude of diversity in root distribution within the soil, termed root system architecture (RSA), is fundamental to determining how this trait contributes to species adaptation in local environments. Roots are the interface between the soil environment and the shoot system and therefore play a key role in anchorage, resource uptake, and stress resilience. Previously, we presented the GLO-Roots (Growth and Luminescence Observatory for Roots) system to study the RSA of soil-grown \textit{Arabidopsis thaliana} plants from germination to maturity (Rellán-Álvarez et al. 2015). In this study, we present the automation of GLO-Roots using robotics and the development of image analysis pipelines in order to examine the temporal dynamic regulation of RSA and the broader natural variation of RSA in Arabidopsis, over time. These datasets describe the developmental dynamics of two independent panels of accessions and reveal highly complex and polygenic RSA traits that show significant correlation with climate variables of the accessions' respective origins.},
journal = {eLife},
issn = {2050-084X},
publisher = {eLife Sciences Publications, Ltd},
}

J. Weyler, J. Quakernack, P. Lottes, J. Behley, and C. Stachniss, "Joint Plant and Leaf Instance Segmentation on Field-Scale UAV Imagery," IEEE Robotics and Automation Letters, vol. 7, iss. 2, pp. 3787-3794, 2022. doi:10.1109/LRA.2022.3147462
[BibTeX] [PDF] [Video]

@Article{weyler2022ral,
author = {J. Weyler and J. Quakernack and P. Lottes and J. Behley and C. Stachniss},
title = {{Joint Plant and Leaf Instance Segmentation on Field-Scale UAV Imagery}},
journal = {IEEE Robotics and Automation Letters},
year = 2022,
doi = {10.1109/LRA.2022.3147462},
issn = {},
volume = {7},
number = {2},
videourl  = {https://www.youtube.com/watch?v=ZHDWnANXU40},
url={https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/weyler2022ral.pdf},
pages = {3787-3794},
}

L. Nunes, R. Marcuzzi, X. Chen, J. Behley, and C. Stachniss, "SegContrast: 3D Point Cloud Feature Representation Learning through Self-supervised Segment Discrimination," IEEE Robotics and Automation Letters, 2022. doi:10.1109/LRA.2022.3142440
[BibTeX] [PDF] [Code] [Video]

@Article{nunes2022ral,
author = {L. Nunes and R. Marcuzzi and X. Chen and J. Behley and C. Stachniss},
title = {{SegContrast: 3D Point Cloud Feature Representation Learning through Self-supervised Segment Discrimination}},
journal = {IEEE Robotics and Automation Letters},
year = 2022,
doi = {10.1109/LRA.2022.3142440},
issn = {2377-3766},
volume = {},
number = {},
codeurl = {https://github.com/PRBonn/segcontrast/blob/main/README.md},
videourl  = {https://www.youtube.com/watch?v=kotRb_ySnIw},
url={http://www.ipb.uni-bonn.de/pdfs/nunes2022ral-icra.pdf},
pages = {},
}

R. Marcuzzi, L. Nunes, L. Wiesmann, I. Vizzo, J. Behley, and C. Stachniss, "Contrastive Instance Association for 4D Panoptic Segmentation using Sequences of 3D LiDAR Scans," IEEE Robotics and Automation Letters, vol. 7, iss. 2, pp. 1550-1557, 2022. doi:10.1109/LRA.2022.3140439
[BibTeX] [PDF] [Code] [Video]

@Article{marcuzzi2022ral,
author = {R. Marcuzzi and L. Nunes and L. Wiesmann and I. Vizzo and J. Behley and C. Stachniss},
title = {{Contrastive Instance Association for 4D Panoptic Segmentation using Sequences of 3D LiDAR Scans}},
journal = {IEEE Robotics and Automation Letters},
year = 2022,
doi = {10.1109/LRA.2022.3140439},
issn = {2377-3766},
codeurl = {https://github.com/PRBonn/contrastive_association/},
videourl  = {https://www.youtube.com/watch?v=XXwiadwjLp4},
url={https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/marcuzzi2022ral.pdf},
volume = 7,
number = 2,
pages = {1550-1557},
}

2021

A. Ahmadi, M. Halstead, and C. McCool, "Virtual Temporal Samples for Recurrent Neural Networks: Applied to Semantic Segmentation in Agriculture," in Pattern Recognition , Cham, 2021, p. 574–588. doi:10.1007/978-3-030-92659-5_37
[BibTeX] [PDF] [Video]

This paper explores the potential for performing temporal semantic segmentation in the context of agricultural robotics without temporally labelled data. We achieve this by proposing to generate virtual temporal samples from labelled still images. By exploiting the relatively static scene and assuming that the robot (camera) moves we are able to generate virtually labelled temporal sequences with no extra annotation effort. Normally, to train a recurrent neural network (RNN), labelled samples from a video (temporal) sequence are required which is laborious and has stymied work in this direction. By generating virtual temporal samples, we demonstrate that it is possible to train a lightweight RNN to perform semantic segmentation on two challenging agricultural datasets. Our results show that by training a temporal semantic segmenter using virtual samples we can increase the performance by an absolute amount of 4.6 and 4.9 on sweet pepper and sugar beet datasets, respectively. This indicates that our virtual data augmentation technique is able to accurately classify agricultural images temporally without the use of complicated synthetic data generation techniques nor with the overhead of labelling large amounts of temporal sequences.

@InProceedings{10.1007/978-3-030-92659-5_37,
author= {Ahmadi, Alireza and Halstead, Michael and McCool, Chris},
editor= {Bauckhage, Christian and Gall, Juergen and Schwing, Alexander},
title= {Virtual Temporal Samples for Recurrent Neural Networks: Applied to Semantic Segmentation in Agriculture},
booktitle= {Pattern Recognition},
year= {2021},
publisher= {Springer International Publishing},
videourl= {https://www.youtube.com/watch?v=KAU0TF-9img},
doi={10.1007/978-3-030-92659-5_37},
url={https://arxiv.org/pdf/2106.10118v2.pdf},
address= {Cham},
pages= {574--588},
abstract= {This paper explores the potential for performing temporal semantic segmentation in the context of agricultural robotics without temporally labelled data. We achieve this by proposing to generate virtual temporal samples from labelled still images. By exploiting the relatively static scene and assuming that the robot (camera) moves we are able to generate virtually labelled temporal sequences with no extra annotation effort. Normally, to train a recurrent neural network (RNN), labelled samples from a video (temporal) sequence are required which is laborious and has stymied work in this direction. By generating virtual temporal samples, we demonstrate that it is possible to train a lightweight RNN to perform semantic segmentation on two challenging agricultural datasets. Our results show that by training a temporal semantic segmenter using virtual samples we can increase the performance by an absolute amount of 4.6 and 4.9 on sweet pepper and sugar beet datasets, respectively. This indicates that our virtual data augmentation technique is able to accurately classify agricultural images temporally without the use of complicated synthetic data generation techniques nor with the overhead of labelling large amounts of temporal sequences.},
isbn= {978-3-030-92659-5}
}

D. Wallach, T. Palosuo, P. Thorburn, Z. Hochman, E. Gourdain, F. Andrianasolo, S. Asseng, B. Basso, S. Buis, N. Crout, C. Dibari, B. Dumont, R. Ferrise, T. Gaiser, C. Garcia, S. Gayler, A. Ghahramani, S. Hiremath, S. Hoek, H. Horan, G. Hoogenboom, M. Huang, M. Jabloun, P. Jansson, Q. Jing, E. Justes, K. C. Kersebaum, A. Klosterhalfen, M. Launay, E. Lewan, Q. Luo, B. Maestrini, H. Mielenz, M. Moriondo, H. Nariman Zadeh, G. Padovan, J. E. Olesen, A. Poyda, E. Priesack, J. W. M. Pullens, B. Qian, N. Schütze, V. Shelia, A. Souissi, X. Specka, A. K. Srivastava, T. Stella, T. Streck, G. Trombi, E. Wallor, J. Wang, T. K. D. Weber, L. Weihermüller, A. de Wit, T. Wöhling, L. Xiao, C. Zhao, Y. Zhu, and S. J. Seidel, "The chaos in calibrating crop models: Lessons learned from a multi-model calibration exercise," Environmental Modelling & Software, vol. 145, p. 105206, 2021. doi:10.1016/j.envsoft.2021.105206
[BibTeX]

Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in many applications of process-based models and has an important impact on simulated values. We propose a novel method of developing guidelines for calibration of process-based models, based on development of recommendations for calibration of the phenology component of crop models. The approach was based on a multi-model study, where all teams were provided with the same data and asked to return simulations for the same conditions. All teams were asked to document in detail their calibration approach, including choices with respect to criteria for best parameters, choice of parameters to estimate and software. Based on an analysis of the advantages and disadvantages of the various choices, we propose calibration recommendations that cover a comprehensive list of decisions and that are based on actual practices.

@article{WALLACH2021105206,
title = {The chaos in calibrating crop models: Lessons learned from a multi-model calibration exercise},
journal = {Environmental Modelling & Software},
volume = {145},
pages = {105206},
year = {2021},
issn = {1364-8152},
doi = {10.1016/j.envsoft.2021.105206},
author = {Daniel Wallach and Taru Palosuo and Peter Thorburn and Zvi Hochman and Emmanuelle Gourdain and Fety Andrianasolo and Senthold Asseng and Bruno Basso and Samuel Buis and Neil Crout and Camilla Dibari and Benjamin Dumont and Roberto Ferrise and Thomas Gaiser and Cecile Garcia and Sebastian Gayler and Afshin Ghahramani and Santosh Hiremath and Steven Hoek and Heidi Horan and Gerrit Hoogenboom and Mingxia Huang and Mohamed Jabloun and Per-Erik Jansson and Qi Jing and Eric Justes and Kurt Christian Kersebaum and Anne Klosterhalfen and Marie Launay and Elisabet Lewan and Qunying Luo and Bernardo Maestrini and Henrike Mielenz and Marco Moriondo and Hasti {Nariman Zadeh} and Gloria Padovan and Jørgen Eivind Olesen and Arne Poyda and Eckart Priesack and Johannes Wilhelmus Maria Pullens and Budong Qian and Niels Schütze and Vakhtang Shelia and Amir Souissi and Xenia Specka and Amit Kumar Srivastava and Tommaso Stella and Thilo Streck and Giacomo Trombi and Evelyn Wallor and Jing Wang and Tobias K.D. Weber and Lutz Weihermüller and Allard {de Wit} and Thomas Wöhling and Liujun Xiao and Chuang Zhao and Yan Zhu and Sabine J. Seidel},
keywords = {Calibration recommendations, Process-based models, Parameter estimation, Phenology},
abstract = {Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in many applications of process-based models and has an important impact on simulated values. We propose a novel method of developing guidelines for calibration of process-based models, based on development of recommendations for calibration of the phenology component of crop models. The approach was based on a multi-model study, where all teams were provided with the same data and asked to return simulations for the same conditions. All teams were asked to document in detail their calibration approach, including choices with respect to criteria for best parameters, choice of parameters to estimate and software. Based on an analysis of the advantages and disadvantages of the various choices, we propose calibration recommendations that cover a comprehensive list of decisions and that are based on actual practices.}
}

S. Hao, D. Ryu, A. Western, E. Perry, H. Bogena, and H. J. H. Franssen, "Performance of a wheat yield prediction model and factors influencing the performance: A review and meta-analysis," Agricultural Systems, vol. 194, p. 103278, 2021. doi:10.1016/j.agsy.2021.103278
[BibTeX]

CONTEXT Process-based crop models provide ways to predict crop growth, evaluate environmental impacts on crops, test various crop management options, and guide crop breeding. They can be used to explore options for mitigating climate change impacts when combined with climate projections and explore mitigation of environmental impacts of production. The Agricultural Production Systems SIMulator (APSIM) is a widely adopted crop model that offers modules for simulation of various crops, soil processes, climate, and grazing within a modelling system that enables robust addition of new components. OBJECTIVE This study uses APSIM Classic-Wheat as an example to examine yield prediction accuracy of biophysically based crop yield modelling and to analyse the factors influencing the model performance. METHODS We analysed yield prediction results of APSIM Classic-Wheat from 76 published studies across thirteen countries on four continents. In addition, a meta-database of modelled and observed yields from 30 studies was established and used to identify factors that influence yield prediction uncertainty. RESULTS AND CONCLUSIONS Our analysis indicates that, with site-specific calibration, APSIM predicts yield with a root mean squared error (RMSE) smaller than 1 t/ha and a normalised RMSE (NRMSE) of about 28%, across a wide range of environmental conditions for independent evaluation periods. The results show increasing errors in yield with limited modelling information and adverse environmental conditions. Using soil hydraulic parameters derived from site-specific measurements and/or tuning cultivar parameters improves yield prediction accuracy: RMSE decreases from 1.25 t/ha to 0.64 t/ha and NRMSE from 32% to 14%. Lower model accuracy was found where APSIM overestimates yield under high water deficit condition and when it underestimates yield under nitrogen limitation. APSIM severely over-predicts yield when some abiotic stresses such as heatwaves and frost affect the crop growth. SIGNIFICANCE This paper uses APSIM-Wheat as an example to provide perspectives on crop model yield prediction performance under different conditions covering a wide spectrum of management practices, and environments. The findings deepen the understanding of model uncertainty associated with different calibration processes or under various stressed conditions. The results also indicate the need to improve the model's predictive skill by filling functional gaps in the wheat simulations and by assimilating external observations (e.g., biomass information estimated by remote sensing) to adjust the model simulation for stressed crops.

@article{HAO2021103278,
title = {Performance of a wheat yield prediction model and factors influencing the performance: A review and meta-analysis},
journal = {Agricultural Systems},
volume = {194},
pages = {103278},
year = {2021},
issn = {0308-521X},
doi = {10.1016/j.agsy.2021.103278},
author = {Shirui Hao and Dongryeol Ryu and Andrew Western and Eileen Perry and Heye Bogena and Harrie Jan Hendricks Franssen},
keywords = {Cropping system, APSIM classic, Wheat, Yield prediction performance, meta-analysis, Literature review},
abstract = {CONTEXT
Process-based crop models provide ways to predict crop growth, evaluate environmental impacts on crops, test various crop management options, and guide crop breeding. They can be used to explore options for mitigating climate change impacts when combined with climate projections and explore mitigation of environmental impacts of production. The Agricultural Production Systems SIMulator (APSIM) is a widely adopted crop model that offers modules for simulation of various crops, soil processes, climate, and grazing within a modelling system that enables robust addition of new components.
OBJECTIVE
This study uses APSIM Classic-Wheat as an example to examine yield prediction accuracy of biophysically based crop yield modelling and to analyse the factors influencing the model performance.
METHODS
We analysed yield prediction results of APSIM Classic-Wheat from 76 published studies across thirteen countries on four continents. In addition, a meta-database of modelled and observed yields from 30 studies was established and used to identify factors that influence yield prediction uncertainty.
RESULTS AND CONCLUSIONS
Our analysis indicates that, with site-specific calibration, APSIM predicts yield with a root mean squared error (RMSE) smaller than 1 t/ha and a normalised RMSE (NRMSE) of about 28%, across a wide range of environmental conditions for independent evaluation periods. The results show increasing errors in yield with limited modelling information and adverse environmental conditions. Using soil hydraulic parameters derived from site-specific measurements and/or tuning cultivar parameters improves yield prediction accuracy: RMSE decreases from 1.25 t/ha to 0.64 t/ha and NRMSE from 32% to 14%. Lower model accuracy was found where APSIM overestimates yield under high water deficit condition and when it underestimates yield under nitrogen limitation. APSIM severely over-predicts yield when some abiotic stresses such as heatwaves and frost affect the crop growth.
SIGNIFICANCE
This paper uses APSIM-Wheat as an example to provide perspectives on crop model yield prediction performance under different conditions covering a wide spectrum of management practices, and environments. The findings deepen the understanding of model uncertainty associated with different calibration processes or under various stressed conditions. The results also indicate the need to improve the model's predictive skill by filling functional gaps in the wheat simulations and by assimilating external observations (e.g., biomass information estimated by remote sensing) to adjust the model simulation for stressed crops.}
}

K. Baylis, T. Heckelei, and H. Storm, "Chapter 83 - Machine learning in agricultural economics," in Handbook of Agricultural Economics, C. B. Barrett and D. R. Just, Eds., Elsevier, 2021, vol. 5, pp. 4551-4612. doi:10.1016/bs.hesagr.2021.10.007
[BibTeX] [PDF]

With the substantial growth in novel data sources and computational power, machine learning holds great potential for economic analysis. However, like any new approach, the strengths and weaknesses of these tools need to be considered when deciding where and how they can be successfully applied. In this chapter, we introduce key ML methods, from penalized regressions, to tree-based methods to neural networks, relating these approaches to common econometric practice. We then explore the potential afforded by ML to fill gaps in our current methodological toolbox. We discuss use cases like the need for flexible functional forms, the use of unstructured data, and large numbers of explanatory variables in both prediction and causal analysis. We also highlight the challenges of complex simulation models including calibration, validation and computational demands and identify places where machine learning can help. We highlight these issues drawing from existing examples in agricultural and applied economics. To unpack the black box of ML, we present numerous approaches used in computer science and statistics for model interpretability. Finally, we highlight some ethical issues around the use of ML. We argue that economists can play a vital role in adapting ML methods for the use in economics by combining them with our domain knowledge of economic mechanisms, and our approach to causal identification.

@InCollection{baylis20214551,
title = {Chapter 83 - Machine learning in agricultural economics},
editor = {Christopher B. Barrett and David R. Just},
series = {Handbook of Agricultural Economics},
publisher = {Elsevier},
volume = {5},
pages = {4551-4612},
year = {2021},
booktitle = {Handbook of Agricultural Economics},
issn = {1574-0072},
doi = {10.1016/bs.hesagr.2021.10.007},
url = {https://www.sciencedirect.com/science/article/pii/S1574007221000074/pdfft?md5=87c1a87629a81f912a4abe70ccad165f&pid=1-s2.0-S1574007221000074-main.pdf},
author = {Kathy Baylis and Thomas Heckelei and Hugo Storm},
keywords = {Machine learning, Agricultural economics, Deep learning, Artificial intelligence, Neural networks, Random forest, Simulation modeling, Causal estimation},
abstract = {With the substantial growth in novel data sources and computational power, machine learning holds great potential for economic analysis. However, like any new approach, the strengths and weaknesses of these tools need to be considered when deciding where and how they can be successfully applied. In this chapter, we introduce key ML methods, from penalized regressions, to tree-based methods to neural networks, relating these approaches to common econometric practice. We then explore the potential afforded by ML to fill gaps in our current methodological toolbox. We discuss use cases like the need for flexible functional forms, the use of unstructured data, and large numbers of explanatory variables in both prediction and causal analysis. We also highlight the challenges of complex simulation models including calibration, validation and computational demands and identify places where machine learning can help. We highlight these issues drawing from existing examples in agricultural and applied economics. To unpack the black box of ML, we present numerous approaches used in computer science and statistics for model interpretability. Finally, we highlight some ethical issues around the use of ML. We argue that economists can play a vital role in adapting ML methods for the use in economics by combining them with our domain knowledge of economic mechanisms, and our approach to causal identification.},
}

E. Stadtländer, T. Horváth, and S. Wrobel, "Learning weakly convex sets in metric spaces," in Joint European Conference on Machine Learning and Knowledge Discovery in Databases , 2021, p. 200–216. doi:10.1007/978-3-030-86520-7_13
[BibTeX] [PDF]

@InProceedings{stadtlander2021learning,
title = {Learning weakly convex sets in metric spaces},
author = {Stadtl{\"a}nder, Eike and Horv{\'a}th, Tam{\'a}s and Wrobel, Stefan},
booktitle = {Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
pages = {200--216},
year = {2021},
url={https://arxiv.org/pdf/2105.06251.pdf},
doi={10.1007/978-3-030-86520-7_13},
organization = {Springer},
}

Z. Akata, A. Geiger, T. Sattler, O. Zatsarynna, J. Sawatzky, and J. Gall, "Discovering Latent Classes for Semi-supervised Semantic Segmentation," in Proc. of German Conference Pattern Recognition, DAGM GCPR 2020 , 2021, p. 202–217. doi:10.1007/978-3-030-71278-5_15
[BibTeX] [PDF]

@InProceedings{akata2021discovering,
title = {Discovering Latent Classes for Semi-supervised Semantic Segmentation},
author = {Akata, Z and Geiger, A and Sattler, T and Zatsarynna, O and Sawatzky, J and Gall, J},
booktitle = {Proc. of German Conference Pattern Recognition, DAGM GCPR 2020},
volume = {12544},
url={https://arxiv.org/pdf/1912.12936.pdf},
doi={10.1007/978-3-030-71278-5_15},
pages = {202--217},
year = {2021},
}

S. Stark, J. Rhyner, J. Börner, A. Kopaleyshvili, and S. Middelhauve, "Bioökonomie in Nordrhein-Westfalen," , 2021. doi:20.500.11811/9399
[BibTeX] [PDF]

@Article{stark2021biookonomie,
title = {Bio{\"o}konomie in Nordrhein-Westfalen},
author = {Stark, Sascha and Rhyner, Jakob and B{\"o}rner, Jan and Kopaleyshvili, Alexandra and Middelhauve, Stella},
url={https://bonndoc.ulb.uni-bonn.de/xmlui/bitstream/handle/20.500.11811/9399/Abschlussbericht_Biooekonomie-in-NRW-1.pdf?sequence=1&isAllowed=y},
doi={20.500.11811/9399},
year = {2021},
publisher = {Zentrum f{\"u}r Entwicklungsforschung (ZEF), Rheinische Friedrich-Wilhelms~…},
}

I. Kögel-Knabner and W. Amelung, "Soil organic matter in major pedogenic soil groups," Geoderma, vol. 384, p. 114785, 2021. doi:10.1016/j.geoderma.2020.114785
[BibTeX] [PDF]

@Article{kogel2021soil,
title = {Soil organic matter in major pedogenic soil groups},
author = {K{\"o}gel-Knabner, Ingrid and Amelung, Wulf},
journal = {Geoderma},
url={https://juser.fz-juelich.de/record/887801/files/KK%2BAM-Geoder%28postprint%29%20%28002%29.pdf},
doi={10.1016/j.geoderma.2020.114785},
volume = {384},
pages = {114785},
year = {2021},
publisher = {Elsevier},
}

A. Mahlein, A. B. A. Alcántara, F. I. R. Yamati, and S. Paulus, "Unlocking the Potential of Hyperspectral Imaging of Plants for Precision Agriculture and Plant Phenotyping," in Optics and Photonics for Sensing the Environment , 2021, p. EW4G–2. doi:10.1364/es.2021.ew4g.2
[BibTeX] [PDF]

@InProceedings{mahlein2021unlocking,
title = {Unlocking the Potential of Hyperspectral Imaging of Plants for Precision Agriculture and Plant Phenotyping},
author = {Mahlein, Anne-Katrin and Alc{\'a}ntara, Abel A Barreto and Yamati, Facundo R Ispizua and Paulus, Stefan},
doi={10.1364/es.2021.ew4g.2},
url={https://www.phenorob.de/wp-content/uploads/2024/08/Mahlein_2021_OSA_Hyperspectral-Imaging.pdf},
booktitle = {Optics and Photonics for Sensing the Environment},
pages = {EW4G--2},
year = {2021},
organization = {Optical Society of America},
}

U. Rascher, K. Acebron, J. Bendig, J. Krämer, V. Krieger, J. Quiros-Vargas, B. Siegmann, and O. Muller, "Measuring and Understanding the Dynamics of Solar-Induced Fluorescence (SIF) and its Relation to Photochemical and Non-Photochemical Energy Dissipation - Scaling Leaf Level Regulation to Canopy and Ecosystem Remote Sensing," in 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS , 2021, pp. 203-206. doi:10.1109/IGARSS47720.2021.9554870
[BibTeX]

@INPROCEEDINGS{9554870,
author={Rascher, U. and Acebron, K. and Bendig, J. and Krämer, J. and Krieger, V. and Quiros-Vargas, J. and Siegmann, B. and Muller, O.},
booktitle={2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS},
title={Measuring and Understanding the Dynamics of Solar-Induced Fluorescence (SIF) and its Relation to Photochemical and Non-Photochemical Energy Dissipation - Scaling Leaf Level Regulation to Canopy and Ecosystem Remote Sensing},
year={2021},
volume={},
number={},
pages={203-206},
doi={10.1109/IGARSS47720.2021.9554870}}

S. Li, Y. Liu, and J. Gall, "Rethinking 3D LiDAR Point Cloud Segmentation," IEEE Transactions on Neural Networks and Learning Systems, pp. 1-12, 2021. doi:10.1109/TNNLS.2021.3132836
[BibTeX] [PDF] [Code]

@ARTICLE{9653732,
author={Li, Shijie and Liu, Yun and Gall, Juergen},
journal={IEEE Transactions on Neural Networks and Learning Systems},
title={Rethinking 3D LiDAR Point Cloud Segmentation},
year={2021},
volume={},
number={},
pages={1-12},
Codeurl={https://github.com/sj-li/UnpNet},
url={https://arxiv.org/pdf/2008.03928.pdf},
doi={10.1109/TNNLS.2021.3132836}}

M. Günder, N. Piatkowski, L. Von Rueden, R. Sifa, and C. Bauckhage, "Towards Intelligent Food Waste Prevention: An Approach Using Scalable and Flexible Harvest Schedule Optimization With Evolutionary Algorithms," IEEE Access, vol. 9, pp. 169044-169055, 2021. doi:10.1109/ACCESS.2021.3137709
[BibTeX] [PDF]

@ARTICLE{9658416,
author={Günder, Maurice and Piatkowski, Nico and Von Rueden, Laura and Sifa, Rafet and Bauckhage, Christian},
journal={IEEE Access},
title={Towards Intelligent Food Waste Prevention: An Approach Using Scalable and Flexible Harvest Schedule Optimization With Evolutionary Algorithms},
year={2021},
volume={9},
number={},
url={https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9658416},
pages={169044-169055},
doi={10.1109/ACCESS.2021.3137709}}

R. Neuville, J. S. Bates, and F. Jonard, "Estimating forest structure from UAV-mounted LiDAR point cloud using machine learning," Remote sensing, vol. 13, iss. 3, p. 352, 2021. doi:10.3390/rs13030352
[BibTeX] [PDF]

@Article{neuville2021estimating,
title = {Estimating forest structure from UAV-mounted LiDAR point cloud using machine learning},
author = {Neuville, Romain and Bates, Jordan Steven and Jonard, Fran{\c{c}}ois},
journal = {Remote sensing},
volume = {13},
number = {3},
pages = {352},
year = {2021},
url={https://www.mdpi.com/2072-4292/13/3/352},
doi={10.3390/rs13030352},
publisher = {Multidisciplinary Digital Publishing Institute},
}

R. A. Rosu, P. Schütt, J. Quenzel, and S. Behnke, "LatticeNet: fast spatio-temporal point cloud segmentation using permutohedral lattices," Autonomous Robots, p. 1–16, 2021. doi:10.1007/s10514-021-09998-1
[BibTeX] [PDF] [Code] [Video]

@Article{rosu2021latticenet,
title = {LatticeNet: fast spatio-temporal point cloud segmentation using permutohedral lattices},
author = {Rosu, Radu Alexandru and Sch{\"u}tt, Peer and Quenzel, Jan and Behnke, Sven},
journal = {Autonomous Robots},
pages = {1--16},
codeurl = {https://github.com/AIS-Bonn/lattice_net},
videourl  = {https://www.youtube.com/watch?v=mbW1IfZSWjc},
url={https://link.springer.com/article/10.1007/s10514-021-09998-1},
doi={10.1007/s10514-021-09998-1},
year = {2021},
publisher = {Springer},
}

P. Kraft, E. E. Rezaei, L. Breuer, F. Ewert, A. Große-Stoltenberg, T. Kleinebecker, D. Seserman, and C. Nendel, "Modelling Agroforestry’s Contributions to People—A Review of Available Models," Agronomy, vol. 11, iss. 11, p. 2106, 2021. doi:10.3390/agronomy11112106
[BibTeX] [PDF]

@Article{kraft2021modelling,
title = {Modelling Agroforestry’s Contributions to People—A Review of Available Models},
author = {Kraft, Philipp and Rezaei, Ehsan Eyshi and Breuer, Lutz and Ewert, Frank and Gro{\ss}e-Stoltenberg, Andr{\'e} and Kleinebecker, Till and Seserman, Diana-Maria and Nendel, Claas},
journal = {Agronomy},
volume = {11},
url={https://www.mdpi.com/2073-4395/11/11/2106},
doi={10.3390/agronomy11112106},
number = {11},
pages = {2106},
year = {2021},
publisher = {Multidisciplinary Digital Publishing Institute},
}

G. Lopez, T. Gaiser, F. Ewert, and A. Srivastava, "Effects of Recent Climate Change on Maize Yield in Southwest Ecuador," Atmosphere, vol. 12, iss. 3, p. 299, 2021. doi:10.3390/atmos12030299
[BibTeX] [PDF]

@Article{lopez2021effects,
title = {Effects of Recent Climate Change on Maize Yield in Southwest Ecuador},
author = {Lopez, Gina and Gaiser, Thomas and Ewert, Frank and Srivastava, Amit},
journal = {Atmosphere},
volume = {12},
number = {3},
pages = {299},
year = {2021},
url={https://www.mdpi.com/2073-4433/12/3/299},
doi={10.3390/atmos12030299},
publisher = {Multidisciplinary Digital Publishing Institute},
}

T. Stella, H. Webber, J. E. Olesen, A. C. Ruane, S. Fronzek, S. Bregaglio, S. Mamidanna, M. Bindi, B. Collins, B. Faye, and others, "Methodology to assess the changing risk of yield failure due to heat and drought stress under climate change," Environmental Research Letters, vol. 16, iss. 10, p. 104033, 2021. doi:10.1088/1748-9326/ac2196
[BibTeX] [PDF]

@Article{stella2021methodology,
title = {Methodology to assess the changing risk of yield failure due to heat and drought stress under climate change},
author = {Stella, Tommaso and Webber, Heidi and Olesen, J{\o}rgen E and Ruane, Alex C and Fronzek, Stefan and Bregaglio, Simone and Mamidanna, Sravya and Bindi, Marco and Collins, Brian and Faye, Babacar and others},
journal = {Environmental Research Letters},
volume = {16},
number = {10},
pages = {104033},
url={https://iopscience.iop.org/article/10.1088/1748-9326/ac2196/pdf},
doi={10.1088/1748-9326/ac2196},
year = {2021},
publisher = {IOP Publishing},
}

S. Li, Y. Zhou, J. Yi, and J. Gall, "Spatial-Temporal Consistency Network for Low-Latency Trajectory Forecasting," in 2021 IEEE/CVF International Conference on Computer Vision (ICCV) , 2021, pp. 1920-1929. doi:10.1109/ICCV48922.2021.00195
[BibTeX] [PDF]

@INPROCEEDINGS{9711490,
author={Li, Shijie and Zhou, Yanying and Yi, Jinhui and Gall, Juergen},
booktitle={2021 IEEE/CVF International Conference on Computer Vision (ICCV)},
title={Spatial-Temporal Consistency Network for Low-Latency Trajectory Forecasting},
year={2021},
volume={},
number={},
url={https://openaccess.thecvf.com/content/ICCV2021/papers/Li_Spatial-Temporal_Consistency_Network_for_Low-Latency_Trajectory_Forecasting_ICCV_2021_paper.pdf},
pages={1920-1929},
doi={10.1109/ICCV48922.2021.00195}}

M. Halstead, A. Ahmadi, C. Smitt, O. Schmittmann, and C. McCool, "Crop Agnostic Monitoring Driven by Deep Learning," Frontiers in plant science, vol. 12, 2021. doi:10.3389/fpls.2021.786702
[BibTeX] [PDF] [Video]

@Article{halstead2021crop,
title = {Crop Agnostic Monitoring Driven by Deep Learning},
author = {Halstead, Michael and Ahmadi, Alireza and Smitt, Claus and Schmittmann, Oliver and McCool, Chris},
journal = {Frontiers in plant science},
volume = {12},
videourl  = {https://www.youtube.com/watch?v=TNrveMCzPgY},
url={https://www.frontiersin.org/articles/10.3389/fpls.2021.786702/full},
doi={10.3389/fpls.2021.786702},
year = {2021},
}

N. Wilke, B. Siegmann, J. A. Postma, O. Muller, V. Krieger, R. Pude, and U. Rascher, "Assessment of plant density for barley and wheat using UAV multispectral imagery for high-throughput field phenotyping," Computers and Electronics in Agriculture, vol. 189, p. 106380, 2021. doi:10.1016/j.compag.2021.106380
[BibTeX] [PDF]

@Article{wilke2021assessment,
title = {Assessment of plant density for barley and wheat using UAV multispectral imagery for high-throughput field phenotyping},
author = {Wilke, Norman and Siegmann, Bastian and Postma, Johannes A and Muller, Onno and Krieger, Vera and Pude, Ralf and Rascher, Uwe},
journal = {Computers and Electronics in Agriculture},
volume = {189},
pages = {106380},
url={https://juser.fz-juelich.de/record/894570/files/Final_Version_angepasst_Review_Process-preprint.pdf},
doi={10.1016/j.compag.2021.106380},
year = {2021},
publisher = {Elsevier},
}

F. He, B. Thiele, D. Kraus, S. Bouteyine, M. Watt, T. Kraska, U. Schurr, and A. J. Kuhn, "Effects of Short-Term Root Cooling before Harvest on Yield and Food Quality of Chinese Broccoli (Brassica oleracea var. Alboglabra Bailey)," Agronomy, vol. 11, iss. 3, p. 577, 2021. doi:10.3390/agronomy11030577
[BibTeX] [PDF]

@Article{he2021effects,
title = {Effects of Short-Term Root Cooling before Harvest on Yield and Food Quality of Chinese Broccoli (Brassica oleracea var. Alboglabra Bailey)},
author = {He, Fang and Thiele, Bj{\"o}rn and Kraus, David and Bouteyine, Souhaila and Watt, Michelle and Kraska, Thorsten and Schurr, Ulrich and Kuhn, Arnd J{\"u}rgen},
journal = {Agronomy},
volume = {11},
number = {3},
pages = {577},
doi = {10.3390/agronomy11030577},
url={https://www.mdpi.com/2073-4395/11/3/577},
year = {2021},
publisher = {Multidisciplinary Digital Publishing Institute},
}

S. De Cannière, M. Herbst, H. Vereecken, P. Defourny, and F. Jonard, "Constraining water limitation of photosynthesis in a crop growth model with sun-induced chlorophyll fluorescence," Remote Sensing of Environment, vol. 267, p. 112722, 2021. doi:10.1016/j.rse.2021.112722
[BibTeX]

@Article{de2021constraining,
title = {Constraining water limitation of photosynthesis in a crop growth model with sun-induced chlorophyll fluorescence},
author = {De Canni{\`e}re, S and Herbst, M and Vereecken, H and Defourny, P and Jonard, Fran{\c{c}}ois},
journal = {Remote Sensing of Environment},
doi={10.1016/j.rse.2021.112722},
volume = {267},
pages = {112722},
year = {2021},
publisher = {Elsevier},
}

M. Landl, A. Haupenthal, D. Leitner, E. Kroener, D. Vetterlein, R. Bol, H. Vereecken, J. Vanderborght, and A. Schnepf, "Simulating rhizodeposition patterns around growing and exuding root systems," in silico Plants, vol. 3, iss. 2, 2021. doi:10.1093/insilicoplants/diab028
[BibTeX] [PDF]

{In this study, we developed a novel model approach to compute the spatio-temporal distribution patterns of rhizodeposits around growing root systems in three dimensions. This model approach allows us to study the evolution of rhizodeposition patterns around complex three-dimensional root systems. Root systems were generated using the root architecture model CPlantBox. The concentration of rhizodeposits at a given location in the soil domain was computed analytically. To simulate the spread of rhizodeposits in the soil, we considered rhizodeposit release from the roots, rhizodeposit diffusion into the soil, rhizodeposit sorption to soil particles and rhizodeposit degradation by microorganisms. To demonstrate the capabilities of our new model approach, we performed simulations for the two example rhizodeposits mucilage and citrate and the example root system Vicia faba. The rhizodeposition model was parameterized using values from the literature. Our simulations showed that the rhizosphere soil volume with rhizodeposit concentrations above a defined threshold value (i.e. the rhizodeposit hotspot volume) exhibited a maximum at intermediate root growth rates. Root branching allowed the rhizospheres of individual roots to overlap, resulting in a greater volume of rhizodeposit hotspots. This was particularly important in the case of citrate, where overlap of rhizodeposition zones accounted for more than half of the total rhizodeposit hotspot volumes. Coupling a root architecture model with a rhizodeposition model allowed us to get a better understanding of the influence of root architecture as well as rhizodeposit properties on the evolution of the spatio-temporal distribution patterns of rhizodeposits around growing root systems.}

@article{10.1093/insilicoplants/diab028,
author = {Landl, Magdalena and Haupenthal, Adrian and Leitner, Daniel and Kroener, Eva and Vetterlein, Doris and Bol, Roland and Vereecken, Harry and Vanderborght, Jan and Schnepf, Andrea},
title = "{Simulating rhizodeposition patterns around growing and exuding root systems}",
journal = {in silico Plants},
volume = {3},
number = {2},
year = {2021},
month = {09},
abstract = "{In this study, we developed a novel model approach to compute the spatio-temporal distribution patterns of rhizodeposits around growing root systems in three dimensions. This model approach allows us to study the evolution of rhizodeposition patterns around complex three-dimensional root systems. Root systems were generated using the root architecture model CPlantBox. The concentration of rhizodeposits at a given location in the soil domain was computed analytically. To simulate the spread of rhizodeposits in the soil, we considered rhizodeposit release from the roots, rhizodeposit diffusion into the soil, rhizodeposit sorption to soil particles and rhizodeposit degradation by microorganisms. To demonstrate the capabilities of our new model approach, we performed simulations for the two example rhizodeposits mucilage and citrate and the example root system Vicia faba. The rhizodeposition model was parameterized using values from the literature. Our simulations showed that the rhizosphere soil volume with rhizodeposit concentrations above a defined threshold value (i.e. the rhizodeposit hotspot volume) exhibited a maximum at intermediate root growth rates. Root branching allowed the rhizospheres of individual roots to overlap, resulting in a greater volume of rhizodeposit hotspots. This was particularly important in the case of citrate, where overlap of rhizodeposition zones accounted for more than half of the total rhizodeposit hotspot volumes. Coupling a root architecture model with a rhizodeposition model allowed us to get a better understanding of the influence of root architecture as well as rhizodeposit properties on the evolution of the spatio-temporal distribution patterns of rhizodeposits around growing root systems.}",
issn = {2517-5025},
doi = {10.1093/insilicoplants/diab028},
url = {https://doi.org/10.1093/insilicoplants/diab028},
note = {diab028},
eprint = {https://academic.oup.com/insilicoplants/article-pdf/3/2/diab028/40681531/diab028.pdf},
}

E. Blagodatskaya, M. Tarkka, C. Knief, R. Koller, S. Peth, V. Schmidt, S. Spielvogel, D. Uteau, M. Weber, and B. S. Razavi, "Bridging Microbial Functional Traits With Localized Process Rates at Soil Interfaces," Frontiers in microbiology, vol. 12, 2021. doi:10.3389/fmicb.2021.625697
[BibTeX] [PDF]

@Article{blagodatskaya2021bridging,
title = {Bridging Microbial Functional Traits With Localized Process Rates at Soil Interfaces},
author = {Blagodatskaya, Evgenia and Tarkka, Mika and Knief, Claudia and Koller, Robert and Peth, Stephan and Schmidt, Volker and Spielvogel, Sandra and Uteau, Daniel and Weber, Matthias and Razavi, Bahar S},
url={https://www.frontiersin.org/articles/10.3389/fmicb.2021.625697/full},
doi={10.3389/fmicb.2021.625697},
journal = {Frontiers in microbiology},
volume = {12},
year = {2021},
publisher = {Frontiers Media SA},
}

C. Pahmeyer, T. Kuhn, and W. Britz, "Single plots or shares of land-How modeling of crop choices in bio-economic farm models influences simulation results," 2021. doi:10.22004/ag.econ.313251
[BibTeX] [PDF]

@TechReport{pahmeyer2021single,
title = {Single plots or shares of land-How modeling of crop choices in bio-economic farm models influences simulation results},
author = {Pahmeyer, Christoph and Kuhn, Till and Britz, Wolfgang},
url={https://ageconsearch.umn.edu/record/313251/},
doi={10.22004/ag.econ.313251},
year = {2021},
}

Z. Zhou, Z. Zhang, A. S. Mason, L. Chen, C. Liu, M. Qin, W. Li, B. Tian, Z. Wu, Z. Lei, and others, "Quantitative traits loci mapping and molecular marker development for total glutenin and glutenin fraction contents in wheat," BMC plant biology, vol. 21, iss. 1, p. 1–13, 2021. doi:10.1186/s12870-021-03221-0
[BibTeX] [PDF]

@Article{zhou2021quantitative,
title = {Quantitative traits loci mapping and molecular marker development for total glutenin and glutenin fraction contents in wheat},
author = {Zhou, Zhengfu and Zhang, Ziwei and Mason, Annaliese S and Chen, Lingzhi and Liu, Congcong and Qin, Maomao and Li, Wenxu and Tian, Baoming and Wu, Zhengqing and Lei, Zhensheng and others},
journal = {BMC plant biology},
volume = {21},
number = {1},
pages = {1--13},
url={https://bmcplantbiol.biomedcentral.com/articles/10.1186/s12870-021-03221-0},
doi={10.1186/s12870-021-03221-0},
year = {2021},
publisher = {BioMed Central},
}

W. Yang, P. Gutbrod, K. Gutbrod, H. Peisker, X. Song, A. Falz, A. J. Meyer, and P. Dörmann, "2-Hydroxy-phytanoyl-CoA lyase (AtHPCL) is involved in phytol metabolism in Arabidopsis," The Plant Journal, 2021. doi:10.1111/tpj.15632
[BibTeX] [PDF]

@Article{yang20212,
title = {2-Hydroxy-phytanoyl-CoA lyase (AtHPCL) is involved in phytol metabolism in Arabidopsis},
author = {Yang, Wentao and Gutbrod, Philipp and Gutbrod, Katharina and Peisker, Helga and Song, Xiaoning and Falz, Anna-Lena and Meyer, Andreas J and D{\"o}rmann, Peter},
journal = {The Plant Journal},
url={https://onlinelibrary.wiley.com/doi/10.1111/tpj.15632},
doi={10.1111/tpj.15632},
year = {2021},
publisher = {Wiley Online Library},
}

M. Schneider, M. Barbosa, A. Ballvora, and J. Leon, "Organic farming-Deep genotyping reveals specific selection footprints in barley populations," , 2021. doi:10.21203/rs.3.rs-266048/v1
[BibTeX] [PDF]

@Article{schneider2021organic,
title = {Organic farming-Deep genotyping reveals specific selection footprints in barley populations},
url={https://www.researchsquare.com/article/rs-266048/v1},
doi={10.21203/rs.3.rs-266048/v1},
author = {Schneider, Michael and Barbosa, Marissa and Ballvora, Agim and Leon, Jens},
year = {2021}
}

J. Schielein, G. P. Frey, J. Miranda, R. A. de Souza, J. Börner, and J. Henderson, "The role of accessibility for land use and land cover change in the Brazilian Amazon," Applied Geography, vol. 132, p. 102419, 2021. doi:10.1016/j.apgeog.2021.102419
[BibTeX]

@Article{schielein2021role,
title = {The role of accessibility for land use and land cover change in the Brazilian Amazon},
author = {Schielein, Johannes and Frey, Gabriel Ponzoni and Miranda, Javier and de Souza, Rodrigo Ant{\^o}nio and Börner, Jan and Henderson, James},
journal = {Applied Geography},
volume = {132},
pages = {102419},
year = {2021},
doi={10.1016/j.apgeog.2021.102419},
publisher = {Elsevier}
}

R. Giudice and J. Börner, "Benefits and costs of incentive-based forest conservation in the Peruvian Amazon," Forest Policy and Economics, vol. 131, p. 102559, 2021. doi:10.1016/j.forpol.2021.102559
[BibTeX]

@Article{giudice2021benefits,
title = {Benefits and costs of incentive-based forest conservation in the Peruvian Amazon},
author = {Giudice, Renzo and B{\"o}rner, Jan},
journal = {Forest Policy and Economics},
volume = {131},
pages = {102559},
doi={10.1016/j.forpol.2021.102559},
year = {2021},
publisher = {Elsevier},
}

L. Peruzzo, X. Liu, C. Chou, E. B. Blancaflor, H. Zhao, X. Ma, B. Mary, V. Iván, M. Weigand, and Y. Wu, "Three-channel electrical impedance spectroscopy for field-scale root phenotyping," The Plant Phenome Journal, vol. 4, iss. 1, p. e20021, 2021. doi:10.1002/ppj2.20021
[BibTeX] [PDF]

@Article{peruzzo2021three,
title = {Three-channel electrical impedance spectroscopy for field-scale root phenotyping},
author = {Peruzzo, Luca and Liu, Xiuwei and Chou, Chunwei and Blancaflor, Elison B and Zhao, Haijun and Ma, Xue-Feng and Mary, Benjamin and Iv{\'a}n, Veronika and Weigand, Maximilian and Wu, Yuxin},
journal = {The Plant Phenome Journal},
volume = {4},
number = {1},
pages = {e20021},
url={https://acsess.onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ppj2.20021},
doi={10.1002/ppj2.20021},
year = {2021},
publisher = {Wiley Online Library},
}

R. Žydelis, L. Weihermüller, and M. Herbst, "Future climate change will accelerate maize phenological development and increase yield in the Nemoral climate," Science of The Total Environment, vol. 784, p. 147175, 2021. doi:10.1016/j.scitotenv.2021.147175
[BibTeX] [PDF]

@Article{vzydelis2021future,
title = {Future climate change will accelerate maize phenological development and increase yield in the Nemoral climate},
author = {{\v{Z}}ydelis, R and Weiherm{\"u}ller, L and Herbst, Michael},
journal = {Science of The Total Environment},
volume = {784},
pages = {147175},
url={https://juser.fz-juelich.de/record/892017/files/Final_accepted_Manuscript.pdf},
doi={10.1016/j.scitotenv.2021.147175},
year = {2021},
publisher = {Elsevier},
}

M. Habib-ur-Rahman, A. Raza, H. E. Ahrends, H. Hüging, and T. Gaiser, "Impact of in-field soil heterogeneity on biomass and yield of winter triticale in an intensively cropped hummocky landscape under temperate climate conditions," Precision agriculture, p. 1–27, 2021. doi:10.1007/s11119-021-09868-x
[BibTeX] [PDF]

@Article{habib2021impact,
title = {Impact of in-field soil heterogeneity on biomass and yield of winter triticale in an intensively cropped hummocky landscape under temperate climate conditions},
author = {Habib-ur-Rahman, Muhammad and Raza, Ahsan and Ahrends, Hella Ellen and H{\"u}ging, Hubert and Gaiser, Thomas},
journal = {Precision agriculture},
pages = {1--27},
url={https://link.springer.com/article/10.1007/s11119-021-09868-x},
doi={10.1007/s11119-021-09868-x},
year = {2021},
publisher = {Springer},
}

F. Navarrete, M. Gallei, A. E. Kornienko, I. Saado, M. Khan, K. Chia, M. A. Darino, J. Bindics, and A. Djamei, "TOPLESS promotes plant immunity by repressing auxin signaling and is targeted by the fungal effector Naked1," Plant Communications, p. 100269, 2021. doi:10.1101/2021.05.04.442566
[BibTeX] [PDF]

@Article{navarrete2021topless,
title = {TOPLESS promotes plant immunity by repressing auxin signaling and is targeted by the fungal effector Naked1},
author = {Navarrete, Fernando and Gallei, Michelle and Kornienko, Aleksandra E and Saado, Indira and Khan, Mamoona and Chia, Khong-Sam and Darino, Martin A and Bindics, Janos and Djamei, Armin},
journal = {Plant Communications},
pages = {100269},
year = {2021},
url={https://www.cell.com/plant-communications/pdf/S2590-3462(21)00183-8.pdf},
doi={10.1101/2021.05.04.442566},
publisher = {Elsevier},
}

J. Krämer, B. Siegmann, T. Kraska, O. Muller, and U. Rascher, "The potential of spatial aggregation to extract remotely sensed sun-induced fluorescence (SIF) of small-sized experimental plots for applications in crop phenotyping," International Journal of Applied Earth Observation and Geoinformation, vol. 104, p. 102565, 2021. doi:10.1016/j.jag.2021.102565
[BibTeX] [PDF]

@Article{kramer2021102565,
title = {The potential of spatial aggregation to extract remotely sensed sun-induced fluorescence (SIF) of small-sized experimental plots for applications in crop phenotyping},
journal = {International Journal of Applied Earth Observation and Geoinformation},
volume = {104},
pages = {102565},
year = {2021},
issn = {0303-2434},
doi = {10.1016/j.jag.2021.102565},
url = {https://www.sciencedirect.com/science/article/pii/S0303243421002725},
author = {Julie Krämer and Bastian Siegmann and Thorsten Kraska and Onno Muller and Uwe Rascher},
keywords = {Sun-induced chlorophyll fluorescence, SIF, Airborne remote sensing, Spatial aggregation, Outlier detection, Hampel identifier, Field phenotyping},
}

H. Gulabani, K. Goswami, Y. Walia, A. Roy, J. J. Noor, K. D. Ingole, M. Kasera, D. Laha, R. F. H. Giehl, G. Schaaf, and S. Bhattacharjee, "Arabidopsis inositol polyphosphate kinases IPK1 and ITPK1 modulate crosstalk between SA-dependent immunity and phosphate-starvation responses," Plant Cell Reports, 2021. doi:10.1007/s00299-021-02812-3
[BibTeX]

@Article{gulabani2021,
title = {Arabidopsis inositol polyphosphate kinases IPK1 and ITPK1 modulate crosstalk between SA-dependent immunity and phosphate-starvation responses},
journal = {Plant Cell Reports},
year = {2021},
doi = {10.1007/s00299-021-02812-3},
author = {Gulabani, Hitika AND Goswami, Krishnendu AND Walia, Yashika And Roy, Abhisha AND Noor, Jewel Jameeta AND Ingole, Kishor D. AND Kasera, Mritunjay AND Laha, Debabrata AND Giehl, Ricardo F. H. AND Schaaf, Gabriel AND Bhattacharjee, Saikat},
}

B. Schmitz, H. Kuhlmann, and C. Holst, "Towards the empirical determination of correlations in terrestrial laser scanner range observations and the comparison of the correlation structure of different scanners," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 182, pp. 228-241, 2021. doi:10.1016/j.isprsjprs.2021.10.012
[BibTeX]

@Article{schmitz2021228,
title = {Towards the empirical determination of correlations in terrestrial laser scanner range observations and the comparison of the correlation structure of different scanners},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
volume = {182},
pages = {228-241},
year = {2021},
issn = {0924-2716},
doi = {10.1016/j.isprsjprs.2021.10.012},
author = {B. Schmitz and H. Kuhlmann and C. Holst},
keywords = {Variance-covariance matrix, Anisotropy, Point cloud, Autocovariance, Stochastic model, Terrestrial laser scanning},
}

Y. Zeng, D. Hao, G. Badgley, A. Damm, U. Rascher, Y. Ryu, J. Johnson, V. Krieger, S. Wu, H. Qiu, Y. Liu, J. A. Berry, and M. Chen, "Estimating near-infrared reflectance of vegetation from hyperspectral data," Remote Sensing of Environment, vol. 267, p. 112723, 2021. doi:10.1016/j.rse.2021.112723
[BibTeX]

@Article{zeng2021112723,
title = {Estimating near-infrared reflectance of vegetation from hyperspectral data},
journal = {Remote Sensing of Environment},
volume = {267},
pages = {112723},
year = {2021},
issn = {0034-4257},
doi = {10.1016/j.rse.2021.112723},
author = {Yelu Zeng and Dalei Hao and Grayson Badgley and Alexander Damm and Uwe Rascher and Youngryel Ryu and Jennifer Johnson and Vera Krieger and Shengbiao Wu and Han Qiu and Yaling Liu and Joseph A. Berry and Min Chen},
keywords = {Solar-induced chlorophyll fluorescence (SIF), Hyperspectral remote sensing, Soil contamination, Near-infrared reflectance of vegetation (NIRv), Singular value decomposition (SVD), Red edge},
}

T. Hertel, I. Elouafi, M. Tanticharoen, and F. Ewert, "Diversification for enhanced food systems resilience," Nature Food, vol. 2, pp. 832-834, 2021. doi:10.1038/s43016-021-00403-9
[BibTeX] [PDF]

@Article{hertel2021,
title = {Diversification for enhanced food systems resilience},
journal = {Nature Food},
volume = {2},
pages = {832-834},
year = {2021},
issn = {2662-1355},
doi = {10.1038/s43016-021-00403-9},
url = {https://www.nature.com/articles/s43016-021-00403-9 },
author = {Hertel, Thomas AND Elouafi, Ismahane AND Tanticharoen, Morakot AND Ewert, Frank},
keywords = {At the field, farm, household and market levels, multiple options exist for diversification of activities, building resilience of food systems to stresses and shocks},
}

D. L. Giammarino, I. Aloise, C. Stachniss, and G. Grisetti, "Visual Place Recognition using LiDAR Intensity Information," in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , 2021. doi:10.1109/iros51168.2021.9636649
[BibTeX] [PDF]

@InProceedings{digiammarino2021iros,
author = {Giammarino, D. L. AND Aloise, I. AND Stachniss, C. AND Grisetti, G.},
title = {{Visual Place Recognition using LiDAR Intensity Information}},
booktitle = {IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)},
year = {2021},
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/digiammarino2021iros.pdf},
doi={10.1109/iros51168.2021.9636649}
}

P. Rottmann, T. Posewsky, A. Milioto, C. Stachniss, and J. Behley, "Improving Monocular Depth Estimation by Semantic Pre-training," in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , 2021. doi:10.1109/iros51168.2021.9636546
[BibTeX] [PDF]

@InProceedings{rottmann2021iros,
author = {P. Rottmann AND T. Posewsky AND A. Milioto AND C. Stachniss AND J. Behley},
title = {{Improving Monocular Depth Estimation by Semantic Pre-training}},
booktitle = {IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)},
year = {2021},
doi={10.1109/iros51168.2021.9636546},
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/rottmann2021iros.pdf},
}

B. Mersch, T. Höllen, K. Zhao, S. C., and R. Roscher, "Maneuver-based Trajectory Prediction for Self-driving Cars Using Spatio-temporal Convolutional Networks," in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , 2021. doi:10.1109/iros51168.2021.9636875
[BibTeX] [PDF] [Video]

@InProceedings{mersch2021iros,
author = {Mersch, B. AND Höllen, T. AND Zhao, K. AND Stachniss C. AND Roscher, R.},
title = {{Maneuver-based Trajectory Prediction for Self-driving Cars Using Spatio-temporal Convolutional Networks}},
booktitle = {IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)},
year = {2021},
doi={10.1109/iros51168.2021.9636875},
videourl  = {https://www.youtube.com/watch?v=5RRGWUn4qAw},
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/mersch2021iros.pdf},
}

F. Stache, J. Westheider, F. Magistri, M. Popović, and C. Stachniss, "Adaptive Path Planning for UAV-based Multi-Resolution Semantic Segmentation," in European Conference on Mobile Robots (ECMR) , 2021. doi:10.1109/ecmr50962.2021.9568788
[BibTeX] [PDF]

@InProceedings{stache2021ecmr,
author = {Stache, F. AND Westheider, J. AND Magistri, F. AND Popović, M. AND Stachniss, C.},
title = {{Adaptive Path Planning for UAV-based Multi-Resolution Semantic Segmentation}},
booktitle = {European Conference on Mobile Robots (ECMR)},
year = {2021},
doi={10.1109/ecmr50962.2021.9568788},
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/stache2021ecmr.pdf},
}

M. Arora, L. Wiesmann, X. Chen, and C. Stachniss, "Mapping the Static Parts of Dynamic Scenes from 3D LiDAR Point Clouds Exploiting Ground Segmentation," in European Conference on Mobile Robots (ECMR) , 2021. doi:10.1109/ecmr50962.2021.9568799
[BibTeX] [PDF] [Code]

@InProceedings{arora2021ecmr,
author = {Arora, M. AND Wiesmann, L. AND Chen, X. AND Stachniss, C. },
title = {{Mapping the Static Parts of Dynamic Scenes from 3D LiDAR Point Clouds Exploiting Ground Segmentation}},
booktitle = {European Conference on Mobile Robots (ECMR)},
year = {2021},
doi={10.1109/ecmr50962.2021.9568799},
codeurl  = {https://github.com/PRBonn/dynamic-point-removal},
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/arora2021ecmr.pdf},
}

R. A. Rosu and S. Behnke, "EasyPBR: A Lightweight Physically-Based Renderer," in Proceedings of 16th International Conference on Computer Graphics Theory and Applications , 2021. doi:10.5220/0010268902450252
[BibTeX] [PDF] [Code] [Video]

@InProceedings{rosu2021grapp,
author = {Rosu, Radu Alexandru AND Behnke, Sven},
title = {{EasyPBR: A Lightweight Physically-Based Renderer}},
booktitle = {Proceedings of 16th International Conference on Computer Graphics Theory and Applications},
doi={10.5220/0010268902450252},
year = {2021},
codeurl  = {https://github.com/AIS-Bonn/easy_pbr},
videourl  = {https://www.youtube.com/watch?v=N20l6dqFcHw},
url = {https://www.ais.uni-bonn.de/papers/GRAPP_2021_Rosu_EasyPBR.pdf},
}

M. Herbst, P. Pohlig, A. Graf, L. Weihermüller, M. Schmidt, J. Vanderborght, and H. Vereecken, "Quantification of water stress induced within-field variability of carbon dioxide fluxes in a sugar beet stand," Agricultural and Forest Meteorology, vol. 297, p. 108242, 2021. doi:10.1016/j.agrformet.2020.108242
[BibTeX] [PDF]

Net ecosystem exchange of carbon dioxide (NEE) and soil respiration at field scale can exhibit considerable spatial variability linked to the heterogeneity of soil properties and state variables. In this study, we measured NEE with the eddy covariance (EC) method in a sugar beet field characterized by high spatial variability in soil physical properties. We further measured NEE and soil respiration by chambers as well as soil water content and temperature at 18 locations within the field. Spatially averaged chamber-measured NEE showed good agreement to the EC-based data. During a dry period high spatial variation of within-field NEE was detected with the chamber method. The coefficient of variation was on average 0.57 during the dry period, with a maximum of 0.72. Based on the depth-specific soil water content measurements the AgroC ecosystem model was inverted for soil hydraulic properties at each of the 18 locations, where soil water content was measured. Analyzing the model results revealed that root water uptake stress was the main driver of spatial and temporal variability in crop development and NEE, whereby the soil coarse material fraction (gravel content) and thickness of the layer above a gravel dominated soil layer were identified as the main influencing soil properties. The chamber-measured NEE and the flux footprint analysis showed that particularly during periods of severe root water uptake stress EC-based measurements would be prone to biases. A combination of the footprint model with the AgroC ecosystem model estimated a bias of 14\% for the dry period and a vegetation period bias of 6\% in relation to the average CO2 flux.

@Article{herbst2021108242,
title = {Quantification of water stress induced within-field variability of carbon dioxide fluxes in a sugar beet stand},
journal = {Agricultural and Forest Meteorology},
volume = {297},
pages = {108242},
year = {2021},
issn = {0168-1923},
doi = {10.1016/j.agrformet.2020.108242},
url = {https://juser.fz-juelich.de/record/889718/files/text.pdf},
author = {M. Herbst and P. Pohlig and A. Graf and L. Weihermüller and M. Schmidt and J. Vanderborght and H. Vereecken},
keywords = {Spatial variation, Net ecosystem exchange, Respiration, Eddy covariance, Crop model, Water stress},
abstract = {Net ecosystem exchange of carbon dioxide (NEE) and soil respiration at field scale can exhibit considerable spatial variability linked to the heterogeneity of soil properties and state variables. In this study, we measured NEE with the eddy covariance (EC) method in a sugar beet field characterized by high spatial variability in soil physical properties. We further measured NEE and soil respiration by chambers as well as soil water content and temperature at 18 locations within the field. Spatially averaged chamber-measured NEE showed good agreement to the EC-based data. During a dry period high spatial variation of within-field NEE was detected with the chamber method. The coefficient of variation was on average 0.57 during the dry period, with a maximum of 0.72. Based on the depth-specific soil water content measurements the AgroC ecosystem model was inverted for soil hydraulic properties at each of the 18 locations, where soil water content was measured. Analyzing the model results revealed that root water uptake stress was the main driver of spatial and temporal variability in crop development and NEE, whereby the soil coarse material fraction (gravel content) and thickness of the layer above a gravel dominated soil layer were identified as the main influencing soil properties. The chamber-measured NEE and the flux footprint analysis showed that particularly during periods of severe root water uptake stress EC-based measurements would be prone to biases. A combination of the footprint model with the AgroC ecosystem model estimated a bias of 14\% for the dry period and a vegetation period bias of 6\% in relation to the average CO2 flux.},
}

A. Dreier, F. Zimmermann, L. Klingbeil, C. Holst, and H. Kuhlmann, "Strategien zur Selektion von Satelliten in kinematischen GNSS-Anwendungen auf Basis von 3D-Umgebungsmodellen," Allgemeine Vermessungs-Nachrichten (AVN), 2021.
[BibTeX] [PDF]

@Article{dreier2021avn,
author = {Dreier, A. AND Zimmermann, F. AND Klingbeil, L. AND Holst, C. AND Kuhlmann, H.},
title = {{Strategien zur Selektion von Satelliten in kinematischen GNSS-Anwendungen auf Basis von 3D-Umgebungsmodellen}},
journal = {Allgemeine Vermessungs-Nachrichten (AVN)},
year = {2021},
url = {https://www.phenorob.de/wp-content/uploads/2024/08/2021_Dreier_Satellitenselektion_AVN.pdf},
}

K. Baylis, T. Heckelei, and T. W. Hertel, "Agricultural Trade and Environmental Sustainability," Annual Review of Resource Economics, vol. 13, iss. 1, pp. 379-401, 2021. doi:10.1146/annurev-resource-101420-090453
[BibTeX] [PDF]

@Article{doi:10.1146/annurev-resource-101420-090453,
author = {Baylis, Kathy and Heckelei, Thomas and Hertel, Thomas W.},
title = {Agricultural Trade and Environmental Sustainability},
journal = {Annual Review of Resource Economics},
volume = {13},
number = {1},
pages = {379-401},
year = {2021},
doi = {10.1146/annurev-resource-101420-090453},
url = { https://doi.org/10.1146/annurev-resource-101420-090453
},
eprint = { https://doi.org/10.1146/annurev-resource-101420-090453},
}

J. Krause, M. Günder, D. Schulz, and R. Gruna, "New active learning algorithms for near-infrared spectroscopy in agricultural applications," at - Automatisierungstechnik, vol. 69, iss. 4, p. 297–306, 2021. doi:doi:10.1515/auto-2020-0143
[BibTeX] [PDF]

@Article{krausegünderschulzgruna+2021+297+306,
author = {Julius Krause and Maurice Günder and Daniel Schulz and Robin Gruna},
doi = {doi:10.1515/auto-2020-0143},
url = {https://publica-rest.fraunhofer.de/server/api/core/bitstreams/902859ac-c1ed-4ba5-8545-decb0147e3ab/content},
title = {New active learning algorithms for near-infrared spectroscopy in agricultural applications},
journal = {at - Automatisierungstechnik},
number = {4},
volume = {69},
year = {2021},
pages = {297--306},
}

V. Sushko, E. Schönfeld, D. Zhang, J. Gall, B. Schiele, and A. Khoreva, "You Only Need Adversarial Supervision for Semantic Image Synthesis," in International Conference on Learning Representations (ICLR) , 2021.
[BibTeX] [PDF] [Code] [Video]

@InProceedings{sushko2021iclr,
author = {Sushko,V. AND Schönfeld, E. AND Zhang, D. AND Gall, J. AND Schiele, B. AND Khoreva, A.},
title = {{You Only Need Adversarial Supervision for Semantic Image Synthesis}},
booktitle = {International Conference on Learning Representations (ICLR)},
year = {2021},
codeurl  = {https://github.com/boschresearch/OASIS},
videourl  = {https://www.youtube.com/watch?v=vUm6vurIwyM},
url = {https://openreview.net/pdf?id=yvQKLaqNE6M},
}

J. Vanderborght, V. Couvreur, F. Meunier, A. Schnepf, H. Vereecken, M. Bouda, and M. Javaux, "From hydraulic root architecture models to macroscopic representations of root hydraulics in soil water flow and land surface models," Hydrology and Earth System Sciences, vol. 25, iss. 9, p. 4835–4860, 2021. doi:10.5194/hess-25-4835-2021
[BibTeX] [PDF]

@Article{hess-25-4835-2021,
author = {Vanderborght, J. and Couvreur, V. and Meunier, F. and Schnepf, A. and Vereecken, H. and Bouda, M. and Javaux, M.},
title = {From hydraulic root architecture models to macroscopic representations of root hydraulics in soil water flow and land surface models},
journal = {Hydrology and Earth System Sciences},
volume = {25},
year = {2021},
number = {9},
pages = {4835--4860},
url = {https://hess.copernicus.org/articles/25/4835/2021/},
doi = {10.5194/hess-25-4835-2021},
}

C. Brogi, J. A. Huisman, L. Weihermüller, M. Herbst, and H. Vereecken, "Added value of geophysics-based soil mapping in agro-ecosystem simulations," SOIL, vol. 7, iss. 1, p. 125–143, 2021. doi:10.5194/soil-7-125-2021
[BibTeX] [PDF]

@Article{soil-7-125-2021,
author = {Brogi, C. and Huisman, J. A. and Weiherm\"uller, L. and Herbst, M. and Vereecken, H.},
title = {Added value of geophysics-based soil mapping in agro-ecosystem simulations},
journal = {SOIL},
volume = {7},
year = {2021},
number = {1},
pages = {125--143},
url = {https://soil.copernicus.org/articles/7/125/2021/},
doi = {10.5194/soil-7-125-2021},
}

S. Li, J. Yi, Y. Abu Farha, and J. Gall, "Pose Refinement Graph Convolutional Network for Skeleton-based Action Recognition," IEEE Robotics and Automation Letters, 2021. doi:10.48550/arXiv.2010.07367
[BibTeX] [PDF] [Code]

@Article{li2021ieee,
author = {Li, S. AND Yi, J. AND Abu Farha, Y. AND Gall, J.},
title = {{Pose Refinement Graph Convolutional Network for Skeleton-based Action Recognition}},
journal = {IEEE Robotics and Automation Letters},
year = {2021},
codeurl  = {https://github.com/sj-li/PR-GCN},
doi = {10.48550/arXiv.2010.07367},
url = {https://arxiv.org/pdf/2010.07367.pdf},
}

X. Chen, S. Li, B. Mersch, L. Wiesmann, J. Gall, J. Behley, and C. Stachniss, "Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data," IEEE Robotics and Automation Letters, 2021. doi:10.1109/LRA.2021.3093567
[BibTeX] [PDF] [Code] [Video]

@Article{chen2021ieee,
author = {Chen, X. AND Li, S. AND Mersch, B. AND Wiesmann, L. AND Gall, J. AND Behley, J. AND Stachniss, C.},
title = {{Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data}},
journal = {IEEE Robotics and Automation Letters},
year = {2021},
doi = {10.1109/LRA.2021.3093567},
codeurl  = {https://github.com/PRBonn/LiDAR-MOS},
videourl  = {https://www.youtube.com/watch?v=NHvsYhk4dhw},
url = {https://arxiv.org/pdf/2105.08971.pdf},
}

T. Stomberg, I. Weber, M. Schmitt, and R. Roscher, "JUNGLE-NET: USING EXPLAINABLE MACHINE LEARNING TO GAIN NEW INSIGHTS INTO THE APPEARANCE OF WILDERNESS IN SATELLITE IMAGERY," ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. V-3-2021, p. 317–324, 2021. doi:10.5194/isprs-annals-V-3-2021-317-2021
[BibTeX] [PDF]

@Article{isprs-annals-v-3-2021-317-2021,
author = {Stomberg, T. and Weber, I. and Schmitt, M. and Roscher, R.},
title = {JUNGLE-NET: USING EXPLAINABLE MACHINE LEARNING TO GAIN NEW INSIGHTS INTO THE APPEARANCE OF WILDERNESS IN SATELLITE IMAGERY},
journal = {ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
volume = {V-3-2021},
year = {2021},
pages = {317--324},
url = {https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-3-2021/317/2021/},
doi = {10.5194/isprs-annals-V-3-2021-317-2021},
}

X. Chen, T. Läbe, A. Milioto, T. Röhling, J. Behley, and C. Stachniss, "OverlapNet: A Siamese Network for Computing LiDAR Scan Similarity with Applications to Loop Closing and Localization," Autonomous Robots, 2021. doi:10.1007/s10514-021-09999-0
[BibTeX] [PDF]

@Article{chen2021autro,
author = {Chen, X. AND Läbe, T. AND Milioto, A. AND Röhling, T. AND Behley, J. AND Stachniss, C.},
title = {{OverlapNet: A Siamese Network for Computing LiDAR Scan Similarity with Applications to Loop Closing and Localization}},
journal = {Autonomous Robots},
year = {2021},
doi = {10.1007/s10514-021-09999-0},
url = {https://link.springer.com/article/10.1007%2Fs10514-021-09999-0#citeas},
}

S. L. Bauke, A. Schnepf, C. von Sperber, N. Orlowski, H. Lewandowski, T. Selzner, F. Tamburini, and W. Amelung, "Tracing uptake and translocation of phosphorus in wheat using oxygen isotopes and mathematical modelling," New Phytologist, vol. 230, iss. 5, pp. 1883-1895, 2021. doi:10.1111/nph.17307
[BibTeX] [PDF]

Summary Understanding P uptake in soil–plant systems requires suitable P tracers. The stable oxygen isotope ratio in phosphate (expressed as δ18OP) is an alternative to radioactive labelling, but the degree to which plants preserve the δ18OP value of the P source is unclear. We hypothesised that the source signal will be preserved in roots rather than shoots. In soil and hydroponic experiments with spring wheat (Triticum aestivum), we replaced irrigation water by 18O-labelled water for up to 10 d. We extracted plant inorganic phosphates with trichloroacetic acid (TCA), assessed temporal dynamics of δ18OTCA-P values after changing to 18O-labelled water and combined the results with a mathematical model. Within 1 wk, full equilibration of δ18OTCA-P values with the isotope value of the water in the growth medium occurred in shoots but not in roots. Model results further indicated that root δ18OTCA-P values were affected by back transport of phosphate from shoots to roots, with a greater contribution of source P at higher temperatures when back transport was reduced. Root δ18OTCA-P partially preserved the source signal, providing an indicator of P uptake sources. This now needs to be tested extensively for different species, soil and climate conditions to enable application in future ecosystem studies.

@Article{https://doi.org/10.1111/nph.17307,
author = {Bauke, Sara L. and Schnepf, Andrea and von Sperber, Christian and Orlowski, Natalie and Lewandowski, Hans and Selzner, Tobias and Tamburini, Federica and Amelung, Wulf},
title = {Tracing uptake and translocation of phosphorus in wheat using oxygen isotopes and mathematical modelling},
journal = {New Phytologist},
volume = {230},
number = {5},
pages = {1883-1895},
keywords = {hydroponics, isotope model, oxygen isotope exchange, phosphate, plant P uptake, roots},
doi = {10.1111/nph.17307},
url = {https://nph.onlinelibrary.wiley.com/doi/abs/10.1111/nph.17307},
eprint = {https://nph.onlinelibrary.wiley.com/doi/pdf/10.1111/nph.17307},
abstract = {Summary Understanding P uptake in soil–plant systems requires suitable P tracers. The stable oxygen isotope ratio in phosphate (expressed as δ18OP) is an alternative to radioactive labelling, but the degree to which plants preserve the δ18OP value of the P source is unclear. We hypothesised that the source signal will be preserved in roots rather than shoots. In soil and hydroponic experiments with spring wheat (Triticum aestivum), we replaced irrigation water by 18O-labelled water for up to 10 d. We extracted plant inorganic phosphates with trichloroacetic acid (TCA), assessed temporal dynamics of δ18OTCA-P values after changing to 18O-labelled water and combined the results with a mathematical model. Within 1 wk, full equilibration of δ18OTCA-P values with the isotope value of the water in the growth medium occurred in shoots but not in roots. Model results further indicated that root δ18OTCA-P values were affected by back transport of phosphate from shoots to roots, with a greater contribution of source P at higher temperatures when back transport was reduced. Root δ18OTCA-P partially preserved the source signal, providing an indicator of P uptake sources. This now needs to be tested extensively for different species, soil and climate conditions to enable application in future ecosystem studies.},
year = {2021},
}

E. Katche, R. Gaebelein, Z. Idris, P. Vasquez-Teuber, Y. Lo, D. Nugent, J. Batley, and A. S. Mason, "Stable, fertile lines produced by hybridization between allotetraploids Brassica juncea (AABB) and Brassica carinata (BBCC) have merged the A and C genomes," New Phytologist, vol. 230, iss. 3, pp. 1242-1257, 2021. doi:10.1111/nph.17225
[BibTeX] [PDF]

Summary Many flowering plant taxa contain allopolyploids that share one or more genomes in common. In the Brassica genus, crop species Brassica juncea and Brassica carinata share the B genome, with 2n = AABB and 2n = BBCC genome complements, respectively. Hybridization results in 2n = BBAC hybrids, but the fate of these hybrids over generations of self-pollination has never been reported. We produced and characterized B. juncea × B. carinata (2n = BBAC) interspecific hybrids over six generations of self-pollination under selection for high fertility using a combination of genotyping, fertility phenotyping, and cytogenetics techniques. Meiotic pairing behaviour improved from 68\% bivalents in the F1 to 98\% in the S5/S6 generations, and initially low hybrid fertility also increased to parent species levels. The S5/S6 hybrids contained an intact B genome (16 chromosomes) plus a new, stable A/C genome (18–20 chromosomes) resulting from recombination and restructuring of A and C-genome chromosomes. Our results provide the first experimental evidence that two genomes can come together to form a new, restructured genome in hybridization events between two allotetraploid species that share a common genome. This mechanism should be considered in interpreting phylogenies in taxa with multiple allopolyploid species.

@Article{https://doi.org/10.1111/nph.17225,
author = {Katche, Elvis and Gaebelein, Roman and Idris, Zurianti and Vasquez-Teuber, Paula and Lo, Yu-tzu and Nugent, David and Batley, Jacqueline and Mason, Annaliese S.},
title = {Stable, fertile lines produced by hybridization between allotetraploids Brassica juncea (AABB) and Brassica carinata (BBCC) have merged the A and C genomes},
journal = {New Phytologist},
volume = {230},
number = {3},
pages = {1242-1257},
keywords = {Brassica, genome rearrangement, homoeologous exchanges, interspecific hybridization, polyploidy},
doi = {10.1111/nph.17225},
url = {https://nph.onlinelibrary.wiley.com/doi/abs/10.1111/nph.17225},
eprint = {https://nph.onlinelibrary.wiley.com/doi/pdf/10.1111/nph.17225},
abstract = {Summary Many flowering plant taxa contain allopolyploids that share one or more genomes in common. In the Brassica genus, crop species Brassica juncea and Brassica carinata share the B genome, with 2n = AABB and 2n = BBCC genome complements, respectively. Hybridization results in 2n = BBAC hybrids, but the fate of these hybrids over generations of self-pollination has never been reported. We produced and characterized B. juncea × B. carinata (2n = BBAC) interspecific hybrids over six generations of self-pollination under selection for high fertility using a combination of genotyping, fertility phenotyping, and cytogenetics techniques. Meiotic pairing behaviour improved from 68\% bivalents in the F1 to 98\% in the S5/S6 generations, and initially low hybrid fertility also increased to parent species levels. The S5/S6 hybrids contained an intact B genome (16 chromosomes) plus a new, stable A/C genome (18–20 chromosomes) resulting from recombination and restructuring of A and C-genome chromosomes. Our results provide the first experimental evidence that two genomes can come together to form a new, restructured genome in hybridization events between two allotetraploid species that share a common genome. This mechanism should be considered in interpreting phylogenies in taxa with multiple allopolyploid species.},
year = {2021},
}

D. Bohnenkamp, J. Behmann, S. Paulus, U. Steiner, and A. Mahlein, "A Hyperspectral Library of Foliar Diseases of Wheat," Phytopathology®, p. PHYTO-09-19-0335-R, 2021. doi:10.1094/PHYTO-09-19-0335-R
[BibTeX]

This work established a hyperspectral library of important foliar diseases of wheat induced by different fungal pathogens, representing a time series from infection to symptom appearance for the purpose of detecting spectral changes. The data were generated under controlled conditions at the leaf scale. The transition from healthy to diseased leaf tissue was assessed, and spectral shifts were identified and used in combination with histological investigations to define developmental stages in pathogenesis for each disease. The spectral signatures of each plant disease that indicate a specific developmental stage during pathogenesis, defined as turning points, were combined into a spectral library. Machine learning analysis methods were applied and compared to test the potential of this library to detect and quantify foliar diseases in hyperspectral images. All evaluated classifiers had high accuracy (≤99\%) for the detection and identification of both biotrophic and necrotrophic fungi. The potential of applying spectral analysis methods in combination with a spectral library for the detection and identification of plant diseases is demonstrated. Further evaluation and development of these algorithms should contribute to a robust detection and identification system for plant diseases at different developmental stages and the promotion and development of site-specific management techniques for plant diseases under field conditions.

@Article{bohnenkamppp,
author = {Bohnenkamp, David and Behmann, Jan and Paulus, Stefan and Steiner, Ulrike and Mahlein, Anne-Katrin},
title = {A Hyperspectral Library of Foliar Diseases of Wheat},
journal = {Phytopathology®},
volume = {0},
number = {0},
pages = {PHYTO-09-19-0335-R},
year = 2021,
doi = {10.1094/PHYTO-09-19-0335-R},
abstract = { This work established a hyperspectral library of important foliar diseases of wheat induced by different fungal pathogens, representing a time series from infection to symptom appearance for the purpose of detecting spectral changes. The data were generated under controlled conditions at the leaf scale. The transition from healthy to diseased leaf tissue was assessed, and spectral shifts were identified and used in combination with histological investigations to define developmental stages in pathogenesis for each disease. The spectral signatures of each plant disease that indicate a specific developmental stage during pathogenesis, defined as turning points, were combined into a spectral library. Machine learning analysis methods were applied and compared to test the potential of this library to detect and quantify foliar diseases in hyperspectral images. All evaluated classifiers had high accuracy (≤99\%) for the detection and identification of both biotrophic and necrotrophic fungi. The potential of applying spectral analysis methods in combination with a spectral library for the detection and identification of plant diseases is demonstrated. Further evaluation and development of these algorithms should contribute to a robust detection and identification system for plant diseases at different developmental stages and the promotion and development of site-specific management techniques for plant diseases under field conditions. },
}

D. Hu, J. Jing, R. J. Snowdon, A. S. Mason, J. Shen, J. Meng, and J. Zou, "Exploring the gene pool of Brassica napus by genomics-based approaches," Plant Biotechnology Journal, vol. 19, iss. 9, pp. 1693-1712, 2021. doi:10.1111/pbi.13636
[BibTeX] [PDF]

Summary De novo allopolyploidization in Brassica provides a very successful model for reconstructing polyploid genomes using progenitor species and relatives to broaden crop gene pools and understand genome evolution after polyploidy, interspecific hybridization and exotic introgression. B. napus (AACC), the major cultivated rapeseed species and the third largest oilseed crop in the world, is a young Brassica species with a limited genetic base resulting from its short history of domestication, cultivation, and intensive selection during breeding for target economic traits. However, the gene pool of B. napus has been significantly enriched in recent decades that has been benefit from worldwide effects by the successful introduction of abundant subgenomic variation and novel genomic variation via intraspecific, interspecific and intergeneric crosses. An important question in this respect is how to utilize such variation to breed crops adapted to the changing global climate. Here, we review the genetic diversity, genome structure, and population-level differentiation of the B. napus gene pool in relation to known exotic introgressions from various species of the Brassicaceae, especially those elucidated by recent genome-sequencing projects. We also summarize progress in gene cloning, trait-marker associations, gene editing, molecular marker-assisted selection and genome-wide prediction, and describe the challenges and opportunities of these techniques as molecular platforms to exploit novel genomic variation and their value in the rapeseed gene pool. Future progress will accelerate the creation and manipulation of genetic diversity with genomic-based improvement, as well as provide novel insights into the neo-domestication of polyploid crops with novel genetic diversity from reconstructed genomes.

@Article{https://doi.org/10.1111/pbi.13636,
author = {Hu, Dandan and Jing, Jinjie and Snowdon, Rod J. and Mason, Annaliese S. and Shen, Jinxiong and Meng, Jinling and Zou, Jun},
title = {Exploring the gene pool of Brassica napus by genomics-based approaches},
journal = {Plant Biotechnology Journal},
volume = {19},
number = {9},
pages = {1693-1712},
keywords = {polyploid crop, Brassica, gene pool, exotic introgressions, genomic changes, genomic-based improvement},
doi = {10.1111/pbi.13636},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/pbi.13636},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/pbi.13636},
abstract = {Summary De novo allopolyploidization in Brassica provides a very successful model for reconstructing polyploid genomes using progenitor species and relatives to broaden crop gene pools and understand genome evolution after polyploidy, interspecific hybridization and exotic introgression. B. napus (AACC), the major cultivated rapeseed species and the third largest oilseed crop in the world, is a young Brassica species with a limited genetic base resulting from its short history of domestication, cultivation, and intensive selection during breeding for target economic traits. However, the gene pool of B. napus has been significantly enriched in recent decades that has been benefit from worldwide effects by the successful introduction of abundant subgenomic variation and novel genomic variation via intraspecific, interspecific and intergeneric crosses. An important question in this respect is how to utilize such variation to breed crops adapted to the changing global climate. Here, we review the genetic diversity, genome structure, and population-level differentiation of the B. napus gene pool in relation to known exotic introgressions from various species of the Brassicaceae, especially those elucidated by recent genome-sequencing projects. We also summarize progress in gene cloning, trait-marker associations, gene editing, molecular marker-assisted selection and genome-wide prediction, and describe the challenges and opportunities of these techniques as molecular platforms to exploit novel genomic variation and their value in the rapeseed gene pool. Future progress will accelerate the creation and manipulation of genetic diversity with genomic-based improvement, as well as provide novel insights into the neo-domestication of polyploid crops with novel genetic diversity from reconstructed genomes.},
year = {2021},
}

F. Navarrete, N. Grujic, A. Stirnberg, I. Saado, D. Aleksza, M. Gallei, H. Adi, A. Alcântara, M. Khan, J. Bindics, M. Trujillo, and A. Djamei, "The Pleiades are a cluster of fungal effectors that inhibit host defenses," PLOS Pathogens, vol. 17, iss. 6, pp. 1-24, 2021. doi:10.1371/journal.ppat.1009641
[BibTeX] [PDF]

Biotrophic plant pathogens secrete effector proteins to manipulate the host physiology. Effectors suppress defenses and induce an environment favorable to disease development. Sequence-based prediction of effector function is impeded by their rapid evolution rate. In the maize pathogen Ustilago maydis, effector-coding genes frequently organize in clusters. Here we describe the functional characterization of the pleiades, a cluster of ten effector genes, by analyzing the micro- and macroscopic phenotype of the cluster deletion and expressing these proteins in planta. Deletion of the pleiades leads to strongly impaired virulence and accumulation of reactive oxygen species (ROS) in infected tissue. Eight of the Pleiades suppress the production of ROS upon perception of pathogen associated molecular patterns (PAMPs). Although functionally redundant, the Pleiades target different host components. The paralogs Taygeta1 and Merope1 suppress ROS production in either the cytoplasm or nucleus, respectively. Merope1 targets and promotes the auto-ubiquitination activity of RFI2, a conserved family of E3 ligases that regulates the production of PAMP-triggered ROS burst in plants.

@Article{10.1371/journal.ppat.1009641,
doi = {10.1371/journal.ppat.1009641},
author = {Navarrete, Fernando AND Grujic, Nenad AND Stirnberg, Alexandra AND Saado, Indira AND Aleksza, David AND Gallei, Michelle AND Adi, Hazem AND Alcântara, André AND Khan, Mamoona AND Bindics, Janos AND Trujillo, Marco AND Djamei, Armin},
journal = {PLOS Pathogens},
publisher = {Public Library of Science},
title = {The Pleiades are a cluster of fungal effectors that inhibit host defenses},
year = {2021},
month = {06},
volume = {17},
url = {https://doi.org/10.1371/journal.ppat.1009641},
pages = {1-24},
abstract = {Biotrophic plant pathogens secrete effector proteins to manipulate the host physiology. Effectors suppress defenses and induce an environment favorable to disease development. Sequence-based prediction of effector function is impeded by their rapid evolution rate. In the maize pathogen Ustilago maydis, effector-coding genes frequently organize in clusters. Here we describe the functional characterization of the pleiades, a cluster of ten effector genes, by analyzing the micro- and macroscopic phenotype of the cluster deletion and expressing these proteins in planta. Deletion of the pleiades leads to strongly impaired virulence and accumulation of reactive oxygen species (ROS) in infected tissue. Eight of the Pleiades suppress the production of ROS upon perception of pathogen associated molecular patterns (PAMPs). Although functionally redundant, the Pleiades target different host components. The paralogs Taygeta1 and Merope1 suppress ROS production in either the cytoplasm or nucleus, respectively. Merope1 targets and promotes the auto-ubiquitination activity of RFI2, a conserved family of E3 ligases that regulates the production of PAMP-triggered ROS burst in plants.},
number = {6},
}

C. Pahmeyer, D. Schäfer, T. Kuhn, and W. Britz, "Data on a synthetic farm population of the German federal state of North Rhine-Westphalia," Data in Brief, vol. 36, p. 107007, 2021. doi:10.1016/j.dib.2021.107007
[BibTeX] [PDF]

Farm-scale and agent-based models draw typically on detailed and preferably spatially explicit single farm data. Data protection standards however restrict or exclude their access, as for example in Germany. We provide data on a synthetic farm population of the German federal state of North Rhine-Westphalia, mainly based on the German Farm Structure Survey 2016 and plot specific crop data from 2019/2020. The population is derived from farm typology at administrative unit level to which the observed plots are allocated afterwards. The data contains 25,858 farms and covers 1.3 million ha of agricultural land, provided at plot scale in a geospatial vector and at farm scale in tabular format. For each plot, the managing farm (including the estimated farm's location), the number of livestock, the cultivated crop, as well as the corresponding administration units are indicated. Furthermore, spatial data such as yield information, soil characteristics, as well as monitoring data on environmental status are attached. The provided data allows for diverse analysis on the farm population in the federal state of North Rhine-Westphalia with farm, agent-based or different bio-physical models. Furthermore, it can serve as a test data set for models which require detailed and spatially explicit farm data.

@Article{pahmeyer2021107007,
title = {Data on a synthetic farm population of the German federal state of North Rhine-Westphalia},
journal = {Data in Brief},
volume = {36},
pages = {107007},
year = {2021},
issn = {2352-3409},
doi = {10.1016/j.dib.2021.107007},
url = {https://www.sciencedirect.com/science/article/pii/S2352340921002912},
author = {Christoph Pahmeyer and David Schäfer and Till Kuhn and Wolfgang Britz},
keywords = {Synthetic farm population, Farm typology, Germany, North Rhine-Westphalia, Farm modeling, Agent-based modeling},
abstract = {Farm-scale and agent-based models draw typically on detailed and preferably spatially explicit single farm data. Data protection standards however restrict or exclude their access, as for example in Germany. We provide data on a synthetic farm population of the German federal state of North Rhine-Westphalia, mainly based on the German Farm Structure Survey 2016 and plot specific crop data from 2019/2020. The population is derived from farm typology at administrative unit level to which the observed plots are allocated afterwards. The data contains 25,858 farms and covers 1.3 million ha of agricultural land, provided at plot scale in a geospatial vector and at farm scale in tabular format. For each plot, the managing farm (including the estimated farm's location), the number of livestock, the cultivated crop, as well as the corresponding administration units are indicated. Furthermore, spatial data such as yield information, soil characteristics, as well as monitoring data on environmental status are attached. The provided data allows for diverse analysis on the farm population in the federal state of North Rhine-Westphalia with farm, agent-based or different bio-physical models. Furthermore, it can serve as a test data set for models which require detailed and spatially explicit farm data.},
}

C. Pahmeyer, T. Kuhn, and W. Britz, "‘Fruchtfolge’: A crop rotation decision support system for optimizing cropping choices with big data and spatially explicit modeling," Computers and Electronics in Agriculture, vol. 181, p. 105948, 2021. doi:10.1016/j.compag.2020.105948
[BibTeX] [PDF] [Video]

Deciding on which crop to plant on a field and how to fertilize it has become increasingly complex as volatile markets, location factors as well as policy restrictions need to be considered simultaneously. To assist farmers in this process, we develop the web-based, open source decision support system ‘Fruchtfolge’ (German for ‘crop rotation’). It provides decision makers with a crop and coarse manure fertilization management recommendation for each field based on the solution of a single farm optimization model. The optimization model accounts for field specific location factors, labor endowments, field-to-farm distances and policy restrictions such as measures linked to the EU Nitrates Directives and the Greening of the EU Common Agricultural Policy. ‘Fruchtfolge’ is user-friendly by automatically including big data related to farm, location and management characteristics and providing instant feedback on alternative management choices. This way, creating a first optimal cropping plan generally requires less than five minutes. We apply the decision support system to a German case study farm which manages fields outside and inside a nitrate sensitive area. In the year 2021, revised fertilization regulations come in force in Germany, which amongst others lowers maximal allowed nitrogen applications relative to crop nutrient needs in nitrate sensitive areas. The regulations provoke profit losses of up to 15\% for the former optimal crop rotation. The optimal adaptation strategy proposed by ‘Fruchfolge’ diminishes this loss to 10\%. The reduction in profit loss clearly underlines the benefits of our support tool to take optimal cropping decisions in a complex environment. Future research should identify barriers of farmers to apply decision support systems and upon availability, integrate more detailed crop and field specific sensor data.

@Article{pahmeyer2021105948,
title = {‘Fruchtfolge’: A crop rotation decision support system for optimizing cropping choices with big data and spatially explicit modeling},
journal = {Computers and Electronics in Agriculture},
volume = {181},
pages = {105948},
year = {2021},
issn = {0168-1699},
doi = {10.1016/j.compag.2020.105948},
videourl  = {https://www.youtube.com/watch?v=twT1HQcJwnU&t=1s},
url = {https://www.sciencedirect.com/science/article/pii/S0168169920331537},
author = {C. Pahmeyer and T. Kuhn and W. Britz},
keywords = {Big data, Decision Support System, Nitrates Directive, Fertilization Ordinance, Farm level simulation model},
abstract = {Deciding on which crop to plant on a field and how to fertilize it has become increasingly complex as volatile markets, location factors as well as policy restrictions need to be considered simultaneously. To assist farmers in this process, we develop the web-based, open source decision support system ‘Fruchtfolge’ (German for ‘crop rotation’). It provides decision makers with a crop and coarse manure fertilization management recommendation for each field based on the solution of a single farm optimization model. The optimization model accounts for field specific location factors, labor endowments, field-to-farm distances and policy restrictions such as measures linked to the EU Nitrates Directives and the Greening of the EU Common Agricultural Policy. ‘Fruchtfolge’ is user-friendly by automatically including big data related to farm, location and management characteristics and providing instant feedback on alternative management choices. This way, creating a first optimal cropping plan generally requires less than five minutes. We apply the decision support system to a German case study farm which manages fields outside and inside a nitrate sensitive area. In the year 2021, revised fertilization regulations come in force in Germany, which amongst others lowers maximal allowed nitrogen applications relative to crop nutrient needs in nitrate sensitive areas. The regulations provoke profit losses of up to 15\% for the former optimal crop rotation. The optimal adaptation strategy proposed by ‘Fruchfolge’ diminishes this loss to 10\%. The reduction in profit loss clearly underlines the benefits of our support tool to take optimal cropping decisions in a complex environment. Future research should identify barriers of farmers to apply decision support systems and upon availability, integrate more detailed crop and field specific sensor data.},
}

E. Cardona Santos, H. Storm, and S. Rasch, "The cost-effectiveness of conservation auctions in the presence of asset specificity: An agent-based model," Land Use Policy, vol. 102, p. 104907, 2021. doi:10.1016/j.landusepol.2020.104907
[BibTeX]

Payments for Environmental Services are a financial incentive for land users to conserve and restore ecosystems. One of the challenges in their implementation is to maximize their cost-effectiveness, or put in other words, to maximize the provision of environmental services for a given budget. This study focuses on two aspects that endanger the cost-effectiveness of such schemes: asymmetric information and asset specificity. If land users are better informed about their own provision costs, compared to the agency, they can increase their rents by demanding higher payments. The presence of asset specificity makes land users vulnerable to being harmed by opportunism. To compensate this risk, they could require higher payments or an exante compensation, likely to compromise compliance. Auctions are claimed to reduce informational rents by revealing land users’ true provision costs. However, their costeffectiveness has been shown to deteriorate if they are repeated over time because bidders can learn and adapt their strategies. Social interaction is particularly important in this context, as it allows land users to gather information on the bid cap; and it allows for trust building, which can substitute the costly formulation and enforcement of contracts, and thus reduce contracting costs. So far, there are only few studies analyzing the effect of asset specificity on the cost-effectiveness of auctions. Our study fills this gap using an agent-based model to analyze the cost-effectiveness of uniform and discriminatory one-shot and repeated auctions. In our model, land users are assumed to be embedded in a social network through which they can interact and learn. Our results suggest that repeated auctions can increase the cost-effectiveness of payments schemes in the presence of asset specificity despite of learning effects over time if land users face liquidity constraints and high time preferences.

@Article{cardonasantos2021104907,
title = {The cost-effectiveness of conservation auctions in the presence of asset specificity: An agent-based model},
journal = {Land Use Policy},
volume = {102},
pages = {104907},
year = {2021},
issn = {0264-8377},
doi = {10.1016/j.landusepol.2020.104907},
author = {Elsa {Cardona Santos} and Hugo Storm and Sebastian Rasch},
keywords = {Agent-based modeling, Discriminatory auctions, Uniform auctions, Reforestation, Conservation, Asset specificity, Payments for environmental services, Social interaction, Trust},
abstract = {Payments for Environmental Services are a financial incentive for land users to conserve and restore ecosystems. One of the challenges in their implementation is to maximize their cost-effectiveness, or put in other words, to maximize the provision of environmental services for a given budget. This study focuses on two aspects that endanger the cost-effectiveness of such schemes: asymmetric information and asset specificity. If land users are better informed about their own provision costs, compared to the agency, they can increase their rents by demanding higher payments. The presence of asset specificity makes land users vulnerable to being harmed by opportunism. To compensate this risk, they could require higher payments or an exante compensation, likely to compromise compliance. Auctions are claimed to reduce informational rents by revealing land users’ true provision costs. However, their costeffectiveness has been shown to deteriorate if they are repeated over time because bidders can learn and adapt their strategies. Social interaction is particularly important in this context, as it allows land users to gather information on the bid cap; and it allows for trust building, which can substitute the costly formulation and enforcement of contracts, and thus reduce contracting costs. So far, there are only few studies analyzing the effect of asset specificity on the cost-effectiveness of auctions. Our study fills this gap using an agent-based model to analyze the cost-effectiveness of uniform and discriminatory one-shot and repeated auctions. In our model, land users are assumed to be embedded in a social network through which they can interact and learn. Our results suggest that repeated auctions can increase the cost-effectiveness of payments schemes in the presence of asset specificity despite of learning effects over time if land users face liquidity constraints and high time preferences.},
}

S. Rasch, T. Wünscher, F. Casasola, M. Ibrahim, and H. Storm, "Permanence of PES and the role of social context in the Regional Integrated Silvo-pastoral Ecosystem Management Project in Costa Rica," Ecological Economics, vol. 185, p. 107027, 2021. doi:10.1016/j.ecolecon.2021.107027
[BibTeX] [PDF]

We present rare, empirical evidence on the permanence of land use changes induced by a payments for ecosystem services (PES) program. A follow-up study was conducted a decade after the end of the Regional Integrated Silvo-pastoral Ecosystem Management Project (RISEMP) in Costa Rica. Econometric analysis found that silvo-pastoral practices persisted in the long term and are not reverted. On average there is also no meaningful intensification of practices after payments ceased. However, there is some heterogeneity on the individual level. We find that farms that increase adoption after the end of the project are farms with slower adoption during the project while some farms that decrease adoption are intense adopters. This indicates a pattern of convergence in the long run. Additionally, we challenge the assumption that payments are mono-causally inducing land use change by investigating non-monetary factors associated practice adoption. We find that not only PES explains adoption of silvo-pastoral practices. While it is challenging to establish clear casual linkages, we find that adoption is associated with the number of social ties to other farmers as well as negatively correlated to the exposure to traditional production paradigms measured as membership, as well as peer membership, in producer organisations.

@Article{rasch2021107027,
title = {Permanence of PES and the role of social context in the Regional Integrated Silvo-pastoral Ecosystem Management Project in Costa Rica},
journal = {Ecological Economics},
volume = {185},
pages = {107027},
year = {2021},
issn = {0921-8009},
doi = {10.1016/j.ecolecon.2021.107027},
url = {https://www.sciencedirect.com/science/article/pii/S0921800921000859},
author = {Sebastian Rasch and Tobias Wünscher and Francisco Casasola and Muhammad Ibrahim and Hugo Storm},
abstract = {We present rare, empirical evidence on the permanence of land use changes induced by a payments for ecosystem services (PES) program. A follow-up study was conducted a decade after the end of the Regional Integrated Silvo-pastoral Ecosystem Management Project (RISEMP) in Costa Rica. Econometric analysis found that silvo-pastoral practices persisted in the long term and are not reverted. On average there is also no meaningful intensification of practices after payments ceased. However, there is some heterogeneity on the individual level. We find that farms that increase adoption after the end of the project are farms with slower adoption during the project while some farms that decrease adoption are intense adopters. This indicates a pattern of convergence in the long run. Additionally, we challenge the assumption that payments are mono-causally inducing land use change by investigating non-monetary factors associated practice adoption. We find that not only PES explains adoption of silvo-pastoral practices. While it is challenging to establish clear casual linkages, we find that adoption is associated with the number of social ties to other farmers as well as negatively correlated to the exposure to traditional production paradigms measured as membership, as well as peer membership, in producer organisations.},
}

Y. Yu, L. Weihermüller, A. Klotzsche, L. Lärm, H. Vereecken, and J. A. Huisman, "Sequential and coupled inversion of horizontal borehole ground penetrating radar data to estimate soil hydraulic properties at the field scale," Journal of Hydrology, vol. 596, p. 126010, 2021. doi:10.1016/j.jhydrol.2021.126010
[BibTeX] [PDF]

Horizontal borehole ground penetrating radar (GPR) measurements can provide valuable information on soil water content (SWC) dynamics in the vadose zone, and hence show potential to estimate soil hydraulic properties. In this study, the performance of both sequential and coupled inversion workflows to obtain soil hydraulic properties from time-lapse horizontal borehole GPR data obtained during an infiltration experiment were compared using a synthetic modelling study and the analysis of actual field data. The sequential inversion using the vadose zone flow model HYDRUS-1D directly relied on SWC profiles determined from the travel time of GPR direct waves using the straight-wave approximation. The synthetic modelling study showed that sequential inversion did not provide accurate estimates of the soil hydraulic parameters due to interpretation errors in the estimated SWC near the infiltration front and the ground surface. In contrast, the coupled inversion approach, which combined HYDRUS-1D with a forward model of GPR wave propagation (gprMax3D) and GPR travel time information, provided accurate estimates of the hydraulic properties in the synthetic modelling study. The application of the coupled inversion approach to measured borehole GPR data also resulted in plausible estimates of the soil hydraulic parameters. It was concluded that coupled inversion should be preferred over sequential inversion of time-lapse horizontal borehole GPR data in the presence of strong SWC gradients that occur during infiltration events.

@Article{yu2021126010,
title = {Sequential and coupled inversion of horizontal borehole ground penetrating radar data to estimate soil hydraulic properties at the field scale},
journal = {Journal of Hydrology},
volume = {596},
pages = {126010},
year = {2021},
issn = {0022-1694},
doi = {10.1016/j.jhydrol.2021.126010},
url = {https://juser.fz-juelich.de/record/891908?ln=de},
author = {Yi Yu and Lutz Weihermüller and Anja Klotzsche and Lena Lärm and Harry Vereecken and Johan Alexander Huisman},
keywords = {Ground penetrating radar, Hydrogeophysics, Coupled inversion},
abstract = {Horizontal borehole ground penetrating radar (GPR) measurements can provide valuable information on soil water content (SWC) dynamics in the vadose zone, and hence show potential to estimate soil hydraulic properties. In this study, the performance of both sequential and coupled inversion workflows to obtain soil hydraulic properties from time-lapse horizontal borehole GPR data obtained during an infiltration experiment were compared using a synthetic modelling study and the analysis of actual field data. The sequential inversion using the vadose zone flow model HYDRUS-1D directly relied on SWC profiles determined from the travel time of GPR direct waves using the straight-wave approximation. The synthetic modelling study showed that sequential inversion did not provide accurate estimates of the soil hydraulic parameters due to interpretation errors in the estimated SWC near the infiltration front and the ground surface. In contrast, the coupled inversion approach, which combined HYDRUS-1D with a forward model of GPR wave propagation (gprMax3D) and GPR travel time information, provided accurate estimates of the hydraulic properties in the synthetic modelling study. The application of the coupled inversion approach to measured borehole GPR data also resulted in plausible estimates of the soil hydraulic parameters. It was concluded that coupled inversion should be preferred over sequential inversion of time-lapse horizontal borehole GPR data in the presence of strong SWC gradients that occur during infiltration events.},
}

J. Henderson, J. Godar, G. P. Frey, J. Börner, and T. Gardner, "The Paraguayan Chaco at a crossroads: drivers of an emerging soybean frontier," Regional Environmental Change, vol. 21, 2021. doi:10.1007/s10113-021-01804-z
[BibTeX] [PDF]

@Article{henderson2021,
author = {Henderson, J. AND Godar, J. AND Frey, G. P. AND Börner, J. AND Gardner, T.},
title = {{The Paraguayan Chaco at a crossroads: drivers of an emerging soybean frontier}},
journal = {Regional Environmental Change},
volume = {21},
issue = {3},
year = {2021},
doi = {10.1007/s10113-021-01804-z},
url = {https://link.springer.com/article/10.1007%2Fs10113-021-01804-z#citeas},
}

H. Jorda, K. Huber, A. Kunkel, J. Vanderborght, M. Javaux, C. Oberdörster, K. Hammel, and A. Schnepf, "Mechanistic modeling of pesticide uptake with a 3D plant architecture model," Environmental Science and Pollution Research, vol. 28, p. 55678–55689, 2021. doi:10.1007/s11356-021-14878-3
[BibTeX] [PDF]

@Article{jorda2021espr,
author = {Jorda, H. AND Huber, K. AND Kunkel, A. AND Vanderborght, J. AND Javaux, M. AND Oberdörster, C. AND Hammel, K. AND Schnepf, A.},
title = {{Mechanistic modeling of pesticide uptake with a 3D plant architecture model}},
journal = {Environmental Science and Pollution Research},
volume = {28},
issue = {39},
year = {2021},
doi = {10.1007/s11356-021-14878-3},
pages = {55678–55689},
url = {https://doi.org/10.1007/s11356-021-14878-3},
}

A. Schnepf and X. He, "Rhizosphere 5 - shining light on the world beneath our feet," Plant and Soil, vol. 461, 2021. doi:10.1007/s11104-021-04942-9
[BibTeX] [PDF]

@Article{schnepf2021plso,
author = {Schnepf, A. AND He, X.},
title = {{Rhizosphere 5 - shining light on the world beneath our feet}},
journal = {Plant and Soil},
volume = {461},
issue = {1},
year = {2021},
doi = {10.1007/s11104-021-04942-9},
url = {https://doi.org/10.1007/s11104-021-04942-9},
}

K. Zhank, A. S. Mason, M. A. Farooq, and et. al., "Challenges and prospects for a potential allohexaploid Brassica crop," Theoretical and Applied Genetics, vol. 134, pp. 2711-2726, 2021. doi:10.1007/s00122-021-03845-8
[BibTeX] [PDF]

@Article{zhang2021thapge,
author = {Zhank, K. AND Mason, A. S. AND Farooq, M. A. AND et. al. },
title = {{Challenges and prospects for a potential allohexaploid Brassica crop}},
journal = {Theoretical and Applied Genetics},
volume = {134},
issue = {9},
year = {2021},
doi = {10.1007/s00122-021-03845-8},
pages = {2711-2726},
url = {https://www.phenorob.de/wp-content/uploads/2024/08/Challenges-and-prospects-for-apotential-allohexaploid-Brassicacrop_author_submitted_version.pdf},
}

J. Behley, M. Garbade, A. Milioto, J. Quenzel, S. Behnke, J. Gall, and C. Stachniss, "Towards 3D LiDAR-based semantic scene understanding of 3D point cloud sequences: The SemanticKITTI Dataset," The International Journal of Robotics Research, vol. 40, iss. 8-9, pp. 959-967, 2021. doi:10.1177/02783649211006735
[BibTeX] [PDF]

@Article{doi:10.1177/02783649211006735,
author = {Jens Behley and Martin Garbade and Andres Milioto and Jan Quenzel and Sven Behnke and Jürgen Gall and Cyrill Stachniss},
title = {Towards 3D LiDAR-based semantic scene understanding of 3D point cloud sequences: The SemanticKITTI Dataset},
journal = {The International Journal of Robotics Research},
volume = {40},
number = {8-9},
pages = {959-967},
year = {2021},
doi = {10.1177/02783649211006735},
url = { https://doi.org/10.1177/02783649211006735
},
eprint = { https://doi.org/10.1177/02783649211006735},
}

C. H. Bock, S. J. Pethybridge, J. G. A. Barbedo, P. D. Esker, A. -K. Mahlein, and E. M. Del Ponte, "A phytopathometry glossary for the twenty-first century: towards consistency and precision in intra- and inter-disciplinary dialogues," Tropical Plant Pathology, 2021. doi:10.1007/s40858-021-00454-0
[BibTeX] [PDF]

@Article{bock2021troplapa,
author = {Bock, C. H. AND Pethybridge, S. J. AND Barbedo, J. G. A. AND Esker, P. D. AND Mahlein, A.-K. AND Del Ponte, E. M.},
title = {{A phytopathometry glossary for the twenty-first century: towards consistency and precision in intra- and inter-disciplinary dialogues}},
journal = {Tropical Plant Pathology},
year = {2021},
doi = {10.1007/s40858-021-00454-0},
url = {https://link.springer.com/article/10.1007/s40858-021-00454-0},
}

N. Behrmann, J. Gall, and M. Noroozi, "Unsupervised Video Representation Learning by Bidirectional Feature Prediction," in Winter Conference on Applications of Computer Vision , 2021.
[BibTeX] [PDF] [Video]

@InProceedings{behrmann2021apcv,
author = {Behrmann, N. AND Gall, J. AND Noroozi, M.},
title = {{Unsupervised Video Representation Learning by Bidirectional Feature Prediction}},
booktitle = {Winter Conference on Applications of Computer Vision},
year = {2021},
videourl = {https://www.youtube.com/watch?v=TFOUCXCz33I},
url = {https://pages.iai.uni-bonn.de/gall_juergen/download/video_representation.pdf},
}

S. Morandage, J. Vanderborght, M. Zörner, G. Cai, D. Leitner, H. Vereecken, and A. Schnepf, "Root architecture development in stony soils," Vadose Zone Journal, vol. 20, iss. 4, p. e20133, 2021. doi:10.1002/vzj2.20133
[BibTeX] [PDF]

Abstract Soils with high stone content represent a challenge to root development, as each stone is an obstacle to root growth. A high stone content also affects soil properties such as temperature or water content, which in turn affects root growth. We investigated the effects of all soil properties combined on root development in the field using both experiments and modeling. Field experiments were carried out in rhizotron facilities during two consecutive growing seasons (wheat [Triticum aestivum L.] and maize [Zea mays L.]) in silty loam soils with high (>50\%) and low (<4\%) stone contents. We extended the CPlantBox root architecture model to explicitly consider the presence of stones and simulated root growth on the plot scale over the whole vegetation period. We found that a linear increase of stone content resulted in a linear decrease of rooting depth across all stone contents and developmental stages considered, whereas rooting depth was only sensitive to cracks below a certain crack density and at earlier growth stages. Moreover, the impact of precipitation-influenced soil strength had a relatively stronger impact on simulated root arrival curves during the vegetation periods than soil temperature. Resulting differences between stony and non-stony soil of otherwise the same crop and weather conditions show similar trends as the differences observed in the rhizotron facilities. The combined belowground effects resulted in differences in characteristic root system measures of up to 48\%. In future work, comparison of absolute values will require including shoot effects—in particular, different carbon availabilities.

@Article{https://doi.org/10.1002/vzj2.20133,
author = {Morandage, Shehan and Vanderborght, Jan and Zörner, Mirjam and Cai, Gaochao and Leitner, Daniel and Vereecken, Harry and Schnepf, Andrea},
title = {Root architecture development in stony soils},
journal = {Vadose Zone Journal},
volume = {20},
number = {4},
pages = {e20133},
doi = {10.1002/vzj2.20133},
url = {https://acsess.onlinelibrary.wiley.com/doi/abs/10.1002/vzj2.20133},
eprint = {https://acsess.onlinelibrary.wiley.com/doi/pdf/10.1002/vzj2.20133},
abstract = {Abstract Soils with high stone content represent a challenge to root development, as each stone is an obstacle to root growth. A high stone content also affects soil properties such as temperature or water content, which in turn affects root growth. We investigated the effects of all soil properties combined on root development in the field using both experiments and modeling. Field experiments were carried out in rhizotron facilities during two consecutive growing seasons (wheat [Triticum aestivum L.] and maize [Zea mays L.]) in silty loam soils with high (>50\%) and low (<4\%) stone contents. We extended the CPlantBox root architecture model to explicitly consider the presence of stones and simulated root growth on the plot scale over the whole vegetation period. We found that a linear increase of stone content resulted in a linear decrease of rooting depth across all stone contents and developmental stages considered, whereas rooting depth was only sensitive to cracks below a certain crack density and at earlier growth stages. Moreover, the impact of precipitation-influenced soil strength had a relatively stronger impact on simulated root arrival curves during the vegetation periods than soil temperature. Resulting differences between stony and non-stony soil of otherwise the same crop and weather conditions show similar trends as the differences observed in the rhizotron facilities. The combined belowground effects resulted in differences in characteristic root system measures of up to 48\%. In future work, comparison of absolute values will require including shoot effects—in particular, different carbon availabilities.},
year = {2021},
}

V. Roslinsky, K. C. Falk, R. Gaebelein, A. S. Mason, and C. Eynck, "Development of B. carinata with super-high erucic acid content through interspecific hybridization," Theoretical and Applied Genetics, vol. 134, pp. 3167-3181, 2021. doi:10.1007/s00122-021-03883-2
[BibTeX] [PDF]

@Article{roslinsky2021thap,
author = {Roslinsky, V. AND Falk, K. C. AND Gaebelein, R. AND Mason, A. S. AND Eynck, C.},
title = {{Development of B. carinata with super-high erucic acid content through interspecific hybridization}},
journal = {Theoretical and Applied Genetics},
volume = {134},
issue = {10},
year = {2021},
doi = {10.1007/s00122-021-03883-2},
pages = {3167-3181},
url = {https://doi.org/10.1007/s00122-021-03883-2},
}

E. Riemer, D. Qiu, D. Laha, R. K. Harmel, P. Gaugler, V. Gaugler, M. Frei, M. Hajirezaei, N. P. Laha, L. Krusenbaum, R. Schneider, A. Saiardi, D. Fiedler, H. J. Jessen, G. Schaaf, and R. F. H. Giehl, "ITPK1 is an InsP6/ADP phosphotransferase that controls phosphate signaling in Arabidopsis," Molecular Plant, 2021. doi:10.1016/j.molp.2021.07.011
[BibTeX] [PDF] [Video]

In plants, phosphate (Pi) homeostasis is regulated by the interaction of PHR transcription factors with stand-alone SPX proteins, which act as sensors for inositol pyrophosphates. In this study, we combined different methods to obtain a comprehensive picture of how inositol (pyro)phosphate metabolism is regulated by Pi and dependent on the inositol phosphate kinase ITPK1. We found that inositol pyrophosphates are more responsive to Pi than lower inositol phosphates, a response conserved across kingdoms. Using the capillary electrophoresis electrospray ionization mass spectrometry (CE-ESI-MS) we could separate different InsP7 isomers in Arabidopsis and rice, and identify 4/6-InsP7 and a PP-InsP4 isomer hitherto not reported in plants. We found that the inositol pyrophosphates 1/3-InsP7, 5-InsP7, and InsP8 increase several fold in shoots after Pi resupply and that tissue-specific accumulation of inositol pyrophosphates relies on ITPK1 activities and MRP5-dependent InsP6 compartmentalization. Notably, ITPK1 is critical for Pi-dependent 5-InsP7 and InsP8 synthesis in planta and its activity regulates Pi starvation responses in a PHR-dependent manner. Furthermore, we demonstrated that ITPK1-mediated conversion of InsP6 to 5-InsP7 requires high ATP concentrations and that Arabidopsis ITPK1 has an ADP phosphotransferase activity to dephosphorylate specifically 5-InsP7 under low ATP. Collectively, our study provides new insights into Pi-dependent changes in nutritional and energetic states with the synthesis of regulatory inositol pyrophosphates.

@Article{riemer2021,
title = {ITPK1 is an InsP6/ADP phosphotransferase that controls phosphate signaling in Arabidopsis},
journal = {Molecular Plant},
year = {2021},
issn = {1674-2052},
doi = {10.1016/j.molp.2021.07.011},
videourl  = {https://www.youtube.com/watch?v=GGcSjm_ULGQ},
url = {https://www.sciencedirect.com/science/article/pii/S167420522100277X},
author = {Esther Riemer and Danye Qiu and Debabrata Laha and Robert K. Harmel and Philipp Gaugler and Verena Gaugler and Michael Frei and Mohammad-Reza Hajirezaei and Nargis Parvin Laha and Lukas Krusenbaum and Robin Schneider and Adolfo Saiardi and Dorothea Fiedler and Henning J. Jessen and Gabriel Schaaf and Ricardo F.H. Giehl},
keywords = {inositol phosphates, inositol pyrophosphates, phosphate homeostasis, phosphate signaling, inositol 1,3,4-trisphosphate 5/6-kinase 1, diphosphoinositol pentakisphosphate kinase},
abstract = {In plants, phosphate (Pi) homeostasis is regulated by the interaction of PHR transcription factors with stand-alone SPX proteins, which act as sensors for inositol pyrophosphates. In this study, we combined different methods to obtain a comprehensive picture of how inositol (pyro)phosphate metabolism is regulated by Pi and dependent on the inositol phosphate kinase ITPK1. We found that inositol pyrophosphates are more responsive to Pi than lower inositol phosphates, a response conserved across kingdoms. Using the capillary electrophoresis electrospray ionization mass spectrometry (CE-ESI-MS) we could separate different InsP7 isomers in Arabidopsis and rice, and identify 4/6-InsP7 and a PP-InsP4 isomer hitherto not reported in plants. We found that the inositol pyrophosphates 1/3-InsP7, 5-InsP7, and InsP8 increase several fold in shoots after Pi resupply and that tissue-specific accumulation of inositol pyrophosphates relies on ITPK1 activities and MRP5-dependent InsP6 compartmentalization. Notably, ITPK1 is critical for Pi-dependent 5-InsP7 and InsP8 synthesis in planta and its activity regulates Pi starvation responses in a PHR-dependent manner. Furthermore, we demonstrated that ITPK1-mediated conversion of InsP6 to 5-InsP7 requires high ATP concentrations and that Arabidopsis ITPK1 has an ADP phosphotransferase activity to dephosphorylate specifically 5-InsP7 under low ATP. Collectively, our study provides new insights into Pi-dependent changes in nutritional and energetic states with the synthesis of regulatory inositol pyrophosphates.},
}

A. Dreier, J. Janßen, H. Kuhlmann, and L. Klingbeil, "Quality Analysis of Direct Georeferencing in Aspects of Absolute Accuracy and Precision for a UAV-Based Laser Scanning System," Remote Sensing, vol. 13, iss. 18, 2021. doi:10.3390/rs13183564
[BibTeX] [PDF]

The use of UAV-based laser scanning systems is increasing due to the rapid development in sensor technology, especially in applications such as topographic surveys or forestry. One advantage of these multi-sensor systems is the possibility of direct georeferencing of the derived 3D point clouds in a global reference frame without additional information from Ground Control Points (GCPs). This paper addresses the quality analysis of direct georeferencing of a UAV-based laser scanning system focusing on the absolute accuracy and precision of the system. The system investigated is based on the RIEGL miniVUX-SYS and the evaluation uses the estimated point clouds compared to a reference point cloud from Terrestrial Laser Scanning (TLS) for two different study areas. The precision is estimated by multiple repetitions of the same measurement and the use of artificial objects, such as targets and tables, resulting in a standard deviation of <1.2 cm for the horizontal and vertical directions. The absolute accuracy is determined using a point-based evaluation, which results in the RMSE being <2 cm for the horizontal direction and <4 cm for the vertical direction, compared to the TLS reference. The results are consistent for the two different study areas with similar evaluation approaches but different flight planning and processing. In addition, the influence of different Global Navigation Satellite System (GNSS) master stations is investigated and no significant difference was found between Virtual Reference Stations (VRS) and a dedicated master station. Furthermore, to control the orientation of the point cloud, a parameter-based analysis using planes in object space was performed, which showed a good agreement with the reference within the noise level of the point cloud. The calculated quality parameters are all smaller than the manufacturer’s specifications and can be transferred to other multi-sensor systems.

@Article{rs13183564,
author = {Dreier, Ansgar and Janßen, Jannik and Kuhlmann, Heiner and Klingbeil, Lasse},
title = {Quality Analysis of Direct Georeferencing in Aspects of Absolute Accuracy and Precision for a UAV-Based Laser Scanning System},
journal = {Remote Sensing},
volume = {13},
year = {2021},
number = {18},
article-number= {3564},
url = {https://www.mdpi.com/2072-4292/13/18/3564},
issn = {2072-4292},
abstract = {The use of UAV-based laser scanning systems is increasing due to the rapid development in sensor technology, especially in applications such as topographic surveys or forestry. One advantage of these multi-sensor systems is the possibility of direct georeferencing of the derived 3D point clouds in a global reference frame without additional information from Ground Control Points (GCPs). This paper addresses the quality analysis of direct georeferencing of a UAV-based laser scanning system focusing on the absolute accuracy and precision of the system. The system investigated is based on the RIEGL miniVUX-SYS and the evaluation uses the estimated point clouds compared to a reference point cloud from Terrestrial Laser Scanning (TLS) for two different study areas. The precision is estimated by multiple repetitions of the same measurement and the use of artificial objects, such as targets and tables, resulting in a standard deviation of <1.2 cm for the horizontal and vertical directions. The absolute accuracy is determined using a point-based evaluation, which results in the RMSE being <2 cm for the horizontal direction and <4 cm for the vertical direction, compared to the TLS reference. The results are consistent for the two different study areas with similar evaluation approaches but different flight planning and processing. In addition, the influence of different Global Navigation Satellite System (GNSS) master stations is investigated and no significant difference was found between Virtual Reference Stations (VRS) and a dedicated master station. Furthermore, to control the orientation of the point cloud, a parameter-based analysis using planes in object space was performed, which showed a good agreement with the reference within the noise level of the point cloud. The calculated quality parameters are all smaller than the manufacturer’s specifications and can be transferred to other multi-sensor systems.},
doi = {10.3390/rs13183564},
}

L. Drees, L. V. Junker-Frohn, J. Kierdorf, and R. Roscher, "Temporal prediction and evaluation of Brassica growth in the field using conditional generative adversarial networks," Computers and Electronics in Agriculture, vol. 190, p. 106415, 2021. doi:10.1016/j.compag.2021.106415
[BibTeX] [PDF] [Video]

Farmers frequently assess plant growth and performance as basis for making decisions when to take action in the field, such as fertilization, weed control, or harvesting. The prediction of plant growth is a major challenge, as it is affected by numerous and highly variable environmental factors. This paper proposes a novel monitoring approach that comprises high-throughput imaging sensor measurements and their automatic analysis to predict future plant growth. Our approach’s core is a novel machine learning-based generative growth model based on conditional generative adversarial networks, which is able to predict the future appearance of individual plants. In experiments with RGB time series images of laboratory-grown Arabidopsis thaliana and field-grown cauliflower plants, we show that our approach produces realistic, reliable, and reasonable images of future growth stages. The automatic interpretation of the generated images through neural network-based instance segmentation allows the derivation of various phenotypic traits that describe plant growth.

@Article{drees2021106415,
title = {Temporal prediction and evaluation of Brassica growth in the field using conditional generative adversarial networks},
journal = {Computers and Electronics in Agriculture},
volume = {190},
pages = {106415},
year = {2021},
issn = {0168-1699},
videourl  = {https://www.youtube.com/watch?v=ZbfpNQXH0IM&embeds_referring_euri=https%3A%2F%2Fipb.uni-bonn.de%2F&source_ve_path=Mjg2NjY},
doi = {10.1016/j.compag.2021.106415},
url = {https://arxiv.org/pdf/2105.07789},
author = {Lukas Drees and Laura Verena Junker-Frohn and Jana Kierdorf and Ribana Roscher},
keywords = {Generative adversarial networks, Agriculture, Cauliflower, Prediction, Plant growth},
abstract = {Farmers frequently assess plant growth and performance as basis for making decisions when to take action in the field, such as fertilization, weed control, or harvesting. The prediction of plant growth is a major challenge, as it is affected by numerous and highly variable environmental factors. This paper proposes a novel monitoring approach that comprises high-throughput imaging sensor measurements and their automatic analysis to predict future plant growth. Our approach’s core is a novel machine learning-based generative growth model based on conditional generative adversarial networks, which is able to predict the future appearance of individual plants. In experiments with RGB time series images of laboratory-grown Arabidopsis thaliana and field-grown cauliflower plants, we show that our approach produces realistic, reliable, and reasonable images of future growth stages. The automatic interpretation of the generated images through neural network-based instance segmentation allows the derivation of various phenotypic traits that describe plant growth.},
}

B. Siegmann, M. P. Cendrero-Mateo, S. Cogliati, A. Damm, J. Gamon, D. Herrera, C. Jedmowski, L. V. Junker-Frohn, T. Kraska, O. Muller, P. Rademske, C. van der Tol, J. Quiros-Vargas, P. Yang, and U. Rascher, "Downscaling of far-red solar-induced chlorophyll fluorescence of different crops from canopy to leaf level using a diurnal data set acquired by the airborne imaging spectrometer HyPlant," Remote Sensing of Environment, vol. 264, p. 112609, 2021. doi:10.1016/j.rse.2021.112609
[BibTeX] [PDF]

Remote sensing-based measurements of solar-induced chlorophyll fluorescence (SIF) are useful for assessing plant functioning at different spatial and temporal scales. SIF is the most direct measure of photosynthesis and is therefore considered important to advance capacity for the monitoring of gross primary production (GPP) while it has also been suggested that its yield facilitates the early detection of vegetation stress. However, due to the influence of different confounding effects, the apparent SIF signal measured at canopy level differs from the fluorescence emitted at leaf level, which makes its physiological interpretation challenging. One of these effects is the scattering of SIF emitted from leaves on its way through the canopy. The escape fraction (fesc) describes the scattering of SIF within the canopy and corresponds to the ratio of apparent SIF at canopy level to SIF at leaf level. In the present study, the fluorescence correction vegetation index (FCVI) was used to determine fesc of far-red SIF for three structurally different crops (sugar beet, winter wheat, and fruit trees) from a diurnal data set recorded by the airborne imaging spectrometer HyPlant. This unique data set, for the first time, allowed a joint analysis of spatial and temporal dynamics of structural effects and thus the downscaling of far-red SIF from canopy (SIF760canopy) to leaf level (SIF760leaf). For a homogeneous crop such as winter wheat, it seems to be sufficient to determine fesc once a day to reliably scale SIF760 from canopy to leaf level. In contrast, for more complex canopies such as fruit trees, calculating fesc for each observation time throughout the day is strongly recommended. The compensation for structural effects, in combination with normalizing SIF760 to remove the effect of incoming radiation, further allowed the estimation of SIF emission efficiency (εSIF) at leaf level, a parameter directly related to the diurnal variations of plant photosynthetic efficiency.

@Article{siegmann2021112609,
title = {Downscaling of far-red solar-induced chlorophyll fluorescence of different crops from canopy to leaf level using a diurnal data set acquired by the airborne imaging spectrometer HyPlant},
journal = {Remote Sensing of Environment},
volume = {264},
pages = {112609},
year = {2021},
issn = {0034-4257},
doi = {10.1016/j.rse.2021.112609},
url = {https://www.sciencedirect.com/science/article/pii/S0034425721003291},
author = {Bastian Siegmann and Maria Pilar Cendrero-Mateo and Sergio Cogliati and Alexander Damm and John Gamon and David Herrera and Christoph Jedmowski and Laura Verena Junker-Frohn and Thorsten Kraska and Onno Muller and Patrick Rademske and Christiaan {van der Tol} and Juan Quiros-Vargas and Peiqi Yang and Uwe Rascher},
keywords = {Solar-induced chlorophyll fluorescence, SIF, HyPlant, Diurnal course, Fluorescence correction vegetation index, FCVI, Fluorescence escape fraction, Photosynthetically active radiation},
abstract = {Remote sensing-based measurements of solar-induced chlorophyll fluorescence (SIF) are useful for assessing plant functioning at different spatial and temporal scales. SIF is the most direct measure of photosynthesis and is therefore considered important to advance capacity for the monitoring of gross primary production (GPP) while it has also been suggested that its yield facilitates the early detection of vegetation stress. However, due to the influence of different confounding effects, the apparent SIF signal measured at canopy level differs from the fluorescence emitted at leaf level, which makes its physiological interpretation challenging. One of these effects is the scattering of SIF emitted from leaves on its way through the canopy. The escape fraction (fesc) describes the scattering of SIF within the canopy and corresponds to the ratio of apparent SIF at canopy level to SIF at leaf level. In the present study, the fluorescence correction vegetation index (FCVI) was used to determine fesc of far-red SIF for three structurally different crops (sugar beet, winter wheat, and fruit trees) from a diurnal data set recorded by the airborne imaging spectrometer HyPlant. This unique data set, for the first time, allowed a joint analysis of spatial and temporal dynamics of structural effects and thus the downscaling of far-red SIF from canopy (SIF760canopy) to leaf level (SIF760leaf). For a homogeneous crop such as winter wheat, it seems to be sufficient to determine fesc once a day to reliably scale SIF760 from canopy to leaf level. In contrast, for more complex canopies such as fruit trees, calculating fesc for each observation time throughout the day is strongly recommended. The compensation for structural effects, in combination with normalizing SIF760 to remove the effect of incoming radiation, further allowed the estimation of SIF emission efficiency (εSIF) at leaf level, a parameter directly related to the diurnal variations of plant photosynthetic efficiency.},
}

A. Barreto, P. Lottes, F. R. Ispizua Yamati, S. Baumgarten, N. A. Wolf, C. Stachniss, A. Mahlein, and S. Paulus, "Automatic UAV-based counting of seedlings in sugar-beet field and extension to maize and strawberry," Computers and Electronics in Agriculture, vol. 191, p. 106493, 2021. doi:10.1016/j.compag.2021.106493
[BibTeX] [PDF]

Counting crop seedlings is a time-demanding activity involved in diverse agricultural practices like plant cultivating, experimental trials, plant breeding procedures, and weed control. Unmanned Aerial Vehicles (UAVs) carrying RGB cameras are novel tools for automatic field mapping, and the analysis of UAV images by deep learning methods can provide relevant agronomic information. UAV-based camera systems and a deep learning image analysis pipeline are implemented for a fully automated plant counting in sugar beet, maize, and strawberry fields in the present study. Five locations were monitored at different growth stages, and the crop number per plot was automatically predicted by using a fully convolutional network (FCN) pipeline. Our FCN-based approach is a single model for jointly determining both the exact stem location of crop and weed plants and a pixel-wise plant classification considering crop, weed, and soil. To determinate the approach performance, predicted crop counting was compared to visually assessed ground truth data. Results show that UAV-based counting of sugar-beet plants delivers forecast errors lower than 4.6%, and the main factors for performance are related to the intra-row distance and the growth stage. The pipeline’s extension to other crops is possible; the errors of the predictions are lower than 4% under practical field conditions for maize and strawberry fields. This work highlight the feasibility of automatic crop counting, which can reduce manual effort to the farmers.

@Article{barreto2021106493,
title = {Automatic UAV-based counting of seedlings in sugar-beet field and extension to maize and strawberry},
journal = {Computers and Electronics in Agriculture},
volume = {191},
pages = {106493},
year = {2021},
issn = {0168-1699},
doi = {10.1016/j.compag.2021.106493},
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/barreto2021cea.pdf},
author = {Abel Barreto and Philipp Lottes and Facundo Ramón {Ispizua Yamati} and Stephen Baumgarten and Nina Anastasia Wolf and Cyrill Stachniss and Anne-Katrin Mahlein and Stefan Paulus},
keywords = {Deep learning, FCN, UAV, Sugar beet, Plant segmentation, Time-series, Intra-row distance, Growth stage},
abstract = {Counting crop seedlings is a time-demanding activity involved in diverse agricultural practices like plant cultivating, experimental trials, plant breeding procedures, and weed control. Unmanned Aerial Vehicles (UAVs) carrying RGB cameras are novel tools for automatic field mapping, and the analysis of UAV images by deep learning methods can provide relevant agronomic information. UAV-based camera systems and a deep learning image analysis pipeline are implemented for a fully automated plant counting in sugar beet, maize, and strawberry fields in the present study. Five locations were monitored at different growth stages, and the crop number per plot was automatically predicted by using a fully convolutional network (FCN) pipeline. Our FCN-based approach is a single model for jointly determining both the exact stem location of crop and weed plants and a pixel-wise plant classification considering crop, weed, and soil. To determinate the approach performance, predicted crop counting was compared to visually assessed ground truth data. Results show that UAV-based counting of sugar-beet plants delivers forecast errors lower than 4.6%, and the main factors for performance are related to the intra-row distance and the growth stage. The pipeline’s extension to other crops is possible; the errors of the predictions are lower than 4% under practical field conditions for maize and strawberry fields. This work highlight the feasibility of automatic crop counting, which can reduce manual effort to the farmers.},
}

P. Welke, F. Alkhoury, C. Bauckhage, and S. Wrobel, "Decision Snippet Features," in 2020 25th International Conference on Pattern Recognition (ICPR) , 2021, pp. 4260-4267. doi:10.1109/ICPR48806.2021.9412025
[BibTeX] [PDF] [Code] [Video]

@InProceedings{9412025,
author = {Welke, Pascal and Alkhoury, Fouad and Bauckhage, Christian and Wrobel, Stefan},
booktitle = {2020 25th International Conference on Pattern Recognition (ICPR)},
title = {Decision Snippet Features},
year = {2021},
volume = {},
number = {},
pages = {4260-4267},
doi = {10.1109/ICPR48806.2021.9412025},
codeurl  = {https://github.com/pwelke/DecisionSnippetFeatures},
videourl  = {https://www.youtube.com/watch?v=nvPiwI18twk},
url = {https://www.computer.org/csdl/proceedings-article/icpr/2021/09412025/1tmj44Ii4jC},
}

T. Zaenker, C. Lehnert, C. McCool, and M. Bennewitz, "Combining Local and Global Viewpoint Planning for Fruit Coverage," in 2021 European Conference on Mobile Robots (ECMR) , 2021, pp. 1-7. doi:10.1109/ECMR50962.2021.9568836
[BibTeX] [PDF]

@INPROCEEDINGS{9568836,
author={Zaenker, Tobias and Lehnert, Chris and McCool, Chris and Bennewitz, Maren},
booktitle={2021 European Conference on Mobile Robots (ECMR)},
title={Combining Local and Global Viewpoint Planning for Fruit Coverage},
year={2021},
volume={},
number={},
pages={1-7},
url={https://arxiv.org/pdf/2108.08114},
doi={10.1109/ECMR50962.2021.9568836}}

N. Dengler, T. Zaenker, F. Verdoja, and M. Bennewitz, "Online Object-Oriented Semantic Mapping and Map Updating," in 2021 European Conference on Mobile Robots (ECMR) , 2021, pp. 1-7. doi:10.1109/ECMR50962.2021.9568817
[BibTeX] [PDF] [Code] [Video]

@INPROCEEDINGS{9568817,
author={Dengler, Nils and Zaenker, Tobias and Verdoja, Francesco and Bennewitz, Maren},
booktitle={2021 European Conference on Mobile Robots (ECMR)},
title={Online Object-Oriented Semantic Mapping and Map Updating},
year={2021},
codeurl={https://github.com/NilsDengler/sem_mapping},
videourl={https://www.youtube.com/watch?v=JoUEW_-VXq0},
url={https://arxiv.org/pdf/2011.06895},
volume={},
number={},
pages={1-7},
doi={10.1109/ECMR50962.2021.9568817}}

T. Zaenker, C. Smitt, C. McCool, and M. Bennewitz, "Viewpoint Planning for Fruit Size and Position Estimation," in Proc.~of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2021, pp. 3271-3277. doi:10.1109/iros51168.2021.9636701
[BibTeX] [PDF] [Code] [Video]

@InProceedings{zaenker21iros,
author = {T. Zaenker and C. Smitt and C. McCool and M. Bennewitz},
title = {Viewpoint Planning for Fruit Size and Position Estimation},
booktitle = {Proc.~of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
codeurl = {https://github.com/Eruvae/roi_viewpoint_planner},
videourl  = {https://www.youtube.com/watch?v=eOBkLUGstBc},
url={https://arxiv.org/pdf/2011.00275.pdf},
doi={10.1109/iros51168.2021.9636701},
pages={3271-3277},
year = 2021,
}

C. Smitt, M. Halstead, T. Zaenker, M. Bennewitz, and C. McCool, "PATHoBot: A Robot for Glasshouse Crop Phenotyping and Intervention," in Proc.~of the IEEE International Conference on Robotics & Automation (ICRA) , 2021, pp. 2324-2330. doi:10.1109/icra48506.2021.9562047
[BibTeX] [PDF] [Video]

@InProceedings{mccool21icra,
author = {C. Smitt and M. Halstead and T. Zaenker and M. Bennewitz and C. McCool},
title = {{PATHoBot}: {A} Robot for Glasshouse Crop Phenotyping and Intervention},
videourl  ={https://www.youtube.com/watch?v=Q94Ihk3cFQY},
url={https://arxiv.org/pdf/2010.16272.pdf},
doi={10.1109/icra48506.2021.9562047},
booktitle = {Proc.~of the IEEE International Conference on Robotics \& Automation (ICRA)},
year = {2021},
pages={2324-2330}}

R. Baatz, H. J. Hendricks Franssen, E. Euskirchen, D. Sihi, M. Dietze, S. Ciavatta, K. Fennel, H. Beck, G. De Lannoy, V. R. N. Pauwels, A. Raiho, C. Montzka, M. Williams, U. Mishra, C. Poppe, S. Zacharias, A. Lausch, L. Samaniego, K. Van Looy, H. Bogena, M. Adamescu, M. Mirtl, A. Fox, K. Goergen, B. S. Naz, Y. Zeng, and H. Vereecken, "Reanalysis in Earth System Science: Toward Terrestrial Ecosystem Reanalysis," Reviews of Geophysics, vol. 59, iss. 3, p. e2020RG000715, 2021. doi:10.1029/2020RG000715
[BibTeX] [PDF]

Abstract A reanalysis is a physically consistent set of optimally merged simulated model states and historical observational data, using data assimilation. High computational costs for modeled processes and assimilation algorithms has led to Earth system specific reanalysis products for the atmosphere, the ocean and the land separately. Recent developments include the advanced uncertainty quantification and the generation of biogeochemical reanalysis for land and ocean. Here, we review atmospheric and oceanic reanalyzes, and more in detail biogeochemical ocean and terrestrial reanalyzes. In particular, we identify land surface, hydrologic and carbon cycle reanalyzes which are nowadays produced in targeted projects for very specific purposes. Although a future joint reanalysis of land surface, hydrologic, and carbon processes represents an analysis of important ecosystem variables, biotic ecosystem variables are assimilated only to a very limited extent. Continuous data sets of ecosystem variables are needed to explore biotic-abiotic interactions and the response of ecosystems to global change. Based on the review of existing achievements, we identify five major steps required to develop terrestrial ecosystem reanalysis to deliver continuous data streams on ecosystem dynamics.

@Article{https://doi.org/10.1029/2020rg000715,
author = {Baatz, R. and Hendricks Franssen, H. J. and Euskirchen, E. and Sihi, D. and Dietze, M. and Ciavatta, S. and Fennel, K. and Beck, H. and De Lannoy, G. and Pauwels, V. R. N. and Raiho, A. and Montzka, C. and Williams, M. and Mishra, U. and Poppe, C. and Zacharias, S. and Lausch, A. and Samaniego, L. and Van Looy, K. and Bogena, H. and Adamescu, M. and Mirtl, M. and Fox, A. and Goergen, K. and Naz, B. S. and Zeng, Y. and Vereecken, H.},
title = {Reanalysis in Earth System Science: Toward Terrestrial Ecosystem Reanalysis},
journal = {Reviews of Geophysics},
volume = {59},
number = {3},
pages = {e2020RG000715},
keywords = {reanalysis, ecosystem reanalysis, land surface reanalysis, data assimilation, hydrologic reanalysis, carbon cycle reanalysis},
doi = {10.1029/2020RG000715},
url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020RG000715},
eprint = {https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2020RG000715},
note = {e2020RG000715 2020RG000715},
abstract = {Abstract A reanalysis is a physically consistent set of optimally merged simulated model states and historical observational data, using data assimilation. High computational costs for modeled processes and assimilation algorithms has led to Earth system specific reanalysis products for the atmosphere, the ocean and the land separately. Recent developments include the advanced uncertainty quantification and the generation of biogeochemical reanalysis for land and ocean. Here, we review atmospheric and oceanic reanalyzes, and more in detail biogeochemical ocean and terrestrial reanalyzes. In particular, we identify land surface, hydrologic and carbon cycle reanalyzes which are nowadays produced in targeted projects for very specific purposes. Although a future joint reanalysis of land surface, hydrologic, and carbon processes represents an analysis of important ecosystem variables, biotic ecosystem variables are assimilated only to a very limited extent. Continuous data sets of ecosystem variables are needed to explore biotic-abiotic interactions and the response of ecosystems to global change. Based on the review of existing achievements, we identify five major steps required to develop terrestrial ecosystem reanalysis to deliver continuous data streams on ecosystem dynamics.},
year = {2021},
}

D. Schunck, F. Magistri, R. A. Rosu, A. Cornelißen, N. Chebrolu, S. Paulus, J. Léon, S. Behnke, C. Stachniss, H. Kuhlmann, and L. Klingbeil, "Pheno4D: A spatio-temporal dataset of maize and tomato plant point clouds for phenotyping and advanced plant analysis ," PLOS ONE, vol. 16, iss. 8, pp. 1-18, 2021. doi:10.1371/journal.pone.0256340
[BibTeX] [PDF] [Code] [Video]

Understanding the growth and development of individual plants is of central importance in modern agriculture, crop breeding, and crop science. To this end, using 3D data for plant analysis has gained attention over the last years. High-resolution point clouds offer the potential to derive a variety of plant traits, such as plant height, biomass, as well as the number and size of relevant plant organs. Periodically scanning the plants even allows for performing spatio-temporal growth analysis. However, highly accurate 3D point clouds from plants recorded at different growth stages are rare, and acquiring this kind of data is costly. Besides, advanced plant analysis methods from machine learning require annotated training data and thus generate intense manual labor before being able to perform an analysis. To address these issues, we present with this dataset paper a multi-temporal dataset featuring high-resolution registered point clouds of maize and tomato plants, which we manually labeled for computer vision tasks, such as for instance segmentation and 3D reconstruction, providing approximately 260 million labeled 3D points. To highlight the usability of the data and to provide baselines for other researchers, we show a variety of applications ranging from point cloud segmentation to non-rigid registration and surface reconstruction. We believe that our dataset will help to develop new algorithms to advance the research for plant phenotyping, 3D reconstruction, non-rigid registration, and deep learning on raw point clouds. The dataset is freely accessible at https://www.ipb.uni-bonn.de/data/pheno4d/.

@Article{schunck2021plosone,
author = {D. Schunck and F. Magistri and R.A. Rosu and A. Corneli{\ss}en and N. Chebrolu and S. Paulus and J. L\'eon and S. Behnke and C. Stachniss and H. Kuhlmann and L. Klingbeil},
title = {{Pheno4D: A spatio-temporal dataset of maize and tomato plant point clouds for phenotyping and advanced plant analysis }},
journal = {PLOS ONE},
publisher = {Public Library of Science},
year = 2021,
month = {08},
volume = {16},
codeurl = {https://www.ipb.uni-bonn.de/data/pheno4d/index.html},
videourl = {https://www.youtube.com/watch?v=V6FgSwLojow},
url = {https://doi.org/10.1371/journal.pone.0256340},
pages = {1-18},
abstract = {Understanding the growth and development of individual plants is of central importance in modern agriculture, crop breeding, and crop science. To this end, using 3D data for plant analysis has gained attention over the last years. High-resolution point clouds offer the potential to derive a variety of plant traits, such as plant height, biomass, as well as the number and size of relevant plant organs. Periodically scanning the plants even allows for performing spatio-temporal growth analysis. However, highly accurate 3D point clouds from plants recorded at different growth stages are rare, and acquiring this kind of data is costly. Besides, advanced plant analysis methods from machine learning require annotated training data and thus generate intense manual labor before being able to perform an analysis. To address these issues, we present with this dataset paper a multi-temporal dataset featuring high-resolution registered point clouds of maize and tomato plants, which we manually labeled for computer vision tasks, such as for instance segmentation and 3D reconstruction, providing approximately 260 million labeled 3D points. To highlight the usability of the data and to provide baselines for other researchers, we show a variety of applications ranging from point cloud segmentation to non-rigid registration and surface reconstruction. We believe that our dataset will help to develop new algorithms to advance the research for plant phenotyping, 3D reconstruction, non-rigid registration, and deep learning on raw point clouds. The dataset is freely accessible at https://www.ipb.uni-bonn.de/data/pheno4d/.},
number = {8},
doi = {10.1371/journal.pone.0256340},
}

A. T. Leite-Filho, B. S. Soares-Filho, J. L. Davis, G. M. Abrahão, and J. Börner, "Deforestation reduces rainfall and agricultural revenues in the Brazilian Amazon," Nature Communications, vol. 12, iss. 1, p. 2591, 2021. doi:10.1038/s41467-021-22840-7
[BibTeX] [PDF]

It has been suggested that rainfall in the Amazon decreases if forest loss exceeds some threshold, but the specific value of this threshold remains uncertain. Here, we investigate the relationship between historical deforestation and rainfall at different geographical scales across the Southern Brazilian Amazon (SBA). We also assess impacts of deforestation policy scenarios on the region's agriculture. Forest loss of up to 55–60{\%} within 28 km grid cells enhances rainfall, but further deforestation reduces rainfall precipitously. This threshold is lower at larger scales (45–50{\%} at 56 km and 25–30{\%} at 112 km grid cells), while rainfall decreases linearly within 224 km grid cells. Widespread deforestation results in a hydrological and economic negative-sum game, because lower rainfall and agricultural productivity at larger scales outdo local gains. Under a weak governance scenario, SBA may lose 56{\%} of its forests by 2050. Reducing deforestation prevents agricultural losses in SBA up to US{\$} 1 billion annually.

@Article{312312312312,
abstract = {It has been suggested that rainfall in the Amazon decreases if forest loss exceeds some threshold, but the specific value of this threshold remains uncertain. Here, we investigate the relationship between historical deforestation and rainfall at different geographical scales across the Southern Brazilian Amazon (SBA). We also assess impacts of deforestation policy scenarios on the region's agriculture. Forest loss of up to 55--60{\%} within 28 km grid cells enhances rainfall, but further deforestation reduces rainfall precipitously. This threshold is lower at larger scales (45--50{\%} at 56 km and 25--30{\%} at 112 km grid cells), while rainfall decreases linearly within 224 km grid cells. Widespread deforestation results in a hydrological and economic negative-sum game, because lower rainfall and agricultural productivity at larger scales outdo local gains. Under a weak governance scenario, SBA may lose 56{\%} of its forests by 2050. Reducing deforestation prevents agricultural losses in SBA up to US{\$} 1 billion annually.},
author = {Leite-Filho, Argemiro Teixeira and Soares-Filho, Britaldo Silveira and Davis, Juliana Leroy and Abrah{\~a}o, Gabriel Medeiros and B{\"o}rner, Jan},
da = {2021/05/10},
date-added = {2021-06-20 19:44:53 +0000},
date-modified = {2021-06-20 19:44:53 +0000},
doi = {10.1038/s41467-021-22840-7},
id = {Leite-Filho2021},
isbn = {2041-1723},
journal = {Nature Communications},
number = {1},
pages = {2591},
title = {Deforestation reduces rainfall and agricultural revenues in the Brazilian Amazon},
ty = {JOUR},
url = {https://doi.org/10.1038/s41467-021-22840-7},
volume = {12},
year = {2021},
}

D. Schulz, H. Yin, B. Tischbein, S. Verleysdonk, R. Adamou, and N. Kumar, "Land use mapping using Sentinel-1 and Sentinel-2 time series in a heterogeneous landscape in Niger, Sahel," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 178, pp. 97-111, 2021. doi:10.1016/j.isprsjprs.2021.06.005
[BibTeX] [PDF]

Land use maps describe the spatial distribution of natural resources, cultural landscapes, and human settlements, serving as an important planning tool for decision makers. In the Sahel area, such information is valuable for risk management and mitigation in challenging sectors like food security, flood control, and urban planning. Due to its uniform quality across large areas in regular time steps, remote sensing imageries are essential input for producing land use maps. However, spatially and temporally heterogeneous landscapes in Sahel make classification of landscape features difficult. Our overall goal is to create an accurate, high resolution land use map covering Niamey, the capital of Niger and its surroundings which represents the unique landscape features in the Sahel using Sentinel-1 and Sentinel-2 archives. We assessed the performance of three commonly used classifiers (i.e. Maximum Likelihood (ML), Support Vector Machine (SVM) and Random Forest (RF)) for land use classification. To understand the utility of different features from Sentinel-1 and Sentinel-2 imagery for classification, we performed feature selection and compared mapping accuracies with and without feature selection. To leverage the strength of each classifier, we developed a classifier ensemble (CE) map based on the mapping accuracy of each land use class and each classifier. The results of this study showed that the performance of individual classifiers depends on feature selection method and accuracies can be improved by combining different classifiers. The ensemble map had an overall accuracy of 72+-3.9 percent and it was found superior in terms of accuracy particularly with respect to built-up areas compared to the existing global land cover products in the study area. Our classification scheme also better characterized the regional environment in the Sahel. For example, we mapped rice and bare rocks that have important regional implication, which are not included in the existing products. Overall, our approach highlights the potentiality of combining multi-modal imageries and multiple classifiers for mapping a heterogenous environment such as the Sahel with high spatial resolution.

@Article{schulz202197,
title = {Land use mapping using Sentinel-1 and Sentinel-2 time series in a heterogeneous landscape in Niger, Sahel},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
volume = {178},
pages = {97-111},
year = {2021},
issn = {0924-2716},
doi = {10.1016/j.isprsjprs.2021.06.005},
url = {https://www.sciencedirect.com/science/article/pii/S0924271621001635},
author = {Dario Schulz and He Yin and Bernhard Tischbein and Sarah Verleysdonk and Rabani Adamou and Navneet Kumar},
keywords = {Classifier ensemble, Land cover, Feature selection, West Africa, Seasonal change},
abstract = {Land use maps describe the spatial distribution of natural resources, cultural landscapes, and human settlements, serving as an important planning tool for decision makers. In the Sahel area, such information is valuable for risk management and mitigation in challenging sectors like food security, flood control, and urban planning. Due to its uniform quality across large areas in regular time steps, remote sensing imageries are essential input for producing land use maps. However, spatially and temporally heterogeneous landscapes in Sahel make classification of landscape features difficult. Our overall goal is to create an accurate, high resolution land use map covering Niamey, the capital of Niger and its surroundings which represents the unique landscape features in the Sahel using Sentinel-1 and Sentinel-2 archives. We assessed the performance of three commonly used classifiers (i.e. Maximum Likelihood (ML), Support Vector Machine (SVM) and Random Forest (RF)) for land use classification. To understand the utility of different features from Sentinel-1 and Sentinel-2 imagery for classification, we performed feature selection and compared mapping accuracies with and without feature selection. To leverage the strength of each classifier, we developed a classifier ensemble (CE) map based on the mapping accuracy of each land use class and each classifier. The results of this study showed that the performance of individual classifiers depends on feature selection method and accuracies can be improved by combining different classifiers. The ensemble map had an overall accuracy of 72+-3.9 percent and it was found superior in terms of accuracy particularly with respect to built-up areas compared to the existing global land cover products in the study area. Our classification scheme also better characterized the regional environment in the Sahel. For example, we mapped rice and bare rocks that have important regional implication, which are not included in the existing products. Overall, our approach highlights the potentiality of combining multi-modal imageries and multiple classifiers for mapping a heterogenous environment such as the Sahel with high spatial resolution.},
}

M. Gerullis, T. Heckelei, and S. Rasch, "Toward understanding the governance of varietal and genetic diversity," Ecology and Society, vol. 26, iss. 2, 2021. doi:10.5751/ES-12333-260228
[BibTeX] [PDF]

@Article{gerullis2021toward,
title = {Toward understanding the governance of varietal and genetic diversity},
author = {Gerullis, Maria and Heckelei, Thomas and Rasch, Sebastian},
journal = {Ecology and Society},
url={https://www.ecologyandsociety.org/vol26/iss2/art28/},
volume = {26},
number = {2},
year = {2021},
publisher = {The Resilience Alliance},
doi = {10.5751/ES-12333-260228},
}

A. Brugger, P. Schramowski, S. Paulus, U. Steiner, K. Kersting, and A. Mahlein, "Spectral signatures in the UV-range can be combined with secondary plant metabolites by deep learning to characterise barley – powdery mildew interaction," Plant Pathology, 2021. doi:10.1111/ppa.13411
[BibTeX] [PDF]

@Article{bruggerpp,
author = {Brugger, Anna and Schramowski, Patrick and Paulus, Stefan and Steiner, Ulrike and Kersting, Kristian and Mahlein, Anne-Katrin},
year = 2021,
title = {Spectral signatures in the UV-range can be combined with secondary plant metabolites by deep learning to characterise barley – powdery mildew interaction},
journal = {Plant Pathology},
keywords = {Blumeria graminis f.sp. hordei, deep learning, Hordeum vulgare, Hyperspectral imaging, secondary plant metabolites, UV-range},
doi = {10.1111/ppa.13411},
url = {https://bsppjournals.onlinelibrary.wiley.com/doi/abs/10.1111/ppa.13411},
eprint = {https://bsppjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/ppa.13411},
}

T. A. Marton and H. Storm, "The case of organic dairy conversion in Norway: Assessment of multivariate neighbourhood effects," Q Open, vol. 1, iss. 1, 2021. doi:10.1093/qopen/qoab009
[BibTeX] [PDF]

{This study examines the impact of neighbourhood effects and individual farm characteristics on the decision process of organic dairy conversion in Norway, using a unique, spatially explicit farm-level panel set comprising information at the population level from 2003 to 2015. Our results reveal a positive spatial spillover of neighbouring conversion, confirming previous findings. Additionally, we demonstrate that neighbouring organic dairy reversion (i.e. switching back to conventional dairy farming) and organic dairy exits (ceasing to farm altogether) exert notable negative spatial spillovers on organic conversion decisions that have not yet been shown in the literature. If organic dairy production is an important policy goal, such negative spatial spillover requires consideration within policy design and extension.}

@Article{10.1093-qopen-qoab009,
author = {Marton, Tibor A and Storm, Hugo},
title = "The case of organic dairy conversion in Norway: Assessment of multivariate neighbourhood effects",
journal = {Q Open},
volume = {1},
number = {1},
year = {2021},
month = {05},
abstract = "{This study examines the impact of neighbourhood effects and individual farm characteristics on the decision process of organic dairy conversion in Norway, using a unique, spatially explicit farm-level panel set comprising information at the population level from 2003 to 2015. Our results reveal a positive spatial spillover of neighbouring conversion, confirming previous findings. Additionally, we demonstrate that neighbouring organic dairy reversion (i.e. switching back to conventional dairy farming) and organic dairy exits (ceasing to farm altogether) exert notable negative spatial spillovers on organic conversion decisions that have not yet been shown in the literature. If organic dairy production is an important policy goal, such negative spatial spillover requires consideration within policy design and extension.}",
issn = {2633-9048},
doi = {10.1093/qopen/qoab009},
url = {https://doi.org/10.1093/qopen/qoab009},
note = {qoab009},
eprint = {https://academic.oup.com/qopen/article-pdf/1/1/qoab009/38391645/qoab009.pdf},
}

S. J. Seidel, T. Gaiser, H. E. Ahrends, H. Hüging, S. Siebert, S. L. Bauke, M. I. Gocke, M. Koch, K. Schweitzer, G. Schaaf, and F. Ewert, "Crop response to P fertilizer omission under a changing climate - Experimental and modeling results over 115 years of a long-term fertilizer experiment," Field Crops Research, vol. 268, p. 108174, 2021. doi:10.1016/j.fcr.2021.108174
[BibTeX] [PDF]

@Article{seidel2021108174,
title = {Crop response to P fertilizer omission under a changing climate - Experimental and modeling results over 115 years of a long-term fertilizer experiment},
journal = {Field Crops Research},
volume = {268},
pages = {108174},
year = {2021},
issn = {0378-4290},
doi = {10.1016/j.fcr.2021.108174},
url = {https://juser.fz-juelich.de/record/904456/files/Seidel_et_al_2020_resubmission_v1_changes_untracked.pdf},
author = {S.J. Seidel and T. Gaiser and H.E. Ahrends and H. Hüging and S. Siebert and S.L. Bauke and M.I. Gocke and M. Koch and K. Schweitzer and G. Schaaf and F. Ewert},
keywords = {Long-term field experiment, Climate change, Nutrient availability, Soil phosphorus simulation, Crop modeling},
}

O. Spykman, A. Gabriel, M. Ptacek, and M. Gandorfer, "Farmers’ perspectives on field crop robots – Evidence from Bavaria, Germany," Computers and Electronics in Agriculture, vol. 186, p. 106176, 2021. doi:10.1016/j.compag.2021.106176
[BibTeX] [PDF]

Farmers’ attitudes toward field crop robots in a European setting have hardly been studied despite an increasing availability of the technology. Given the relevance of robots for small-scale agriculture, however, their acceptability in regions dominated by small-scale agriculture such as Bavaria, Germany, is of particular interest. Based on a sample of 174 farmers, an exploratory investigation of factors influencing the preference for large or small field crop robots in general and in specific settings and for mode of operation was carried out. Data were gathered using questionnaires at two events including lectures and field demonstrations and analyzed using bivariate tests. Farm size, farming system (organic/conventional), and occupational structure (part-time/full-time) were relevant attributes influencing the evaluation of advantages and disadvantages of field crop robots. Generally, respondents from larger farms focus more on financial benefits from robots and prefer large autonomous tractors. Conversely, small-scale or organic farmers consider environmental benefits of field crop robots relatively more important and favor small robots. Organic farming also positively correlates with the intent to purchase field crop robots within the next five years. More farmers can generally imagine owning small robots as opposed to an autonomous tractor in ten years, but at the same time view autonomous tractors as more suitable for most specified agronomic tasks. Non-purchase options such as contractor services and machinery sharing represent the preferred modes of robot deployment.

@Article{spykman2021106176,
title = {Farmers’ perspectives on field crop robots – Evidence from Bavaria, Germany},
journal = {Computers and Electronics in Agriculture},
volume = {186},
pages = {106176},
year = {2021},
issn = {0168-1699},
doi = {10.1016/j.compag.2021.106176},
url = {https://www.sciencedirect.com/science/article/pii/S0168169921001939},
author = {O. Spykman and A. Gabriel and M. Ptacek and M. Gandorfer},
keywords = {Farmer survey, Attitude, Autonomous, Digitalization, Field crop robot},
abstract = {Farmers’ attitudes toward field crop robots in a European setting have hardly been studied despite an increasing availability of the technology. Given the relevance of robots for small-scale agriculture, however, their acceptability in regions dominated by small-scale agriculture such as Bavaria, Germany, is of particular interest. Based on a sample of 174 farmers, an exploratory investigation of factors influencing the preference for large or small field crop robots in general and in specific settings and for mode of operation was carried out. Data were gathered using questionnaires at two events including lectures and field demonstrations and analyzed using bivariate tests. Farm size, farming system (organic/conventional), and occupational structure (part-time/full-time) were relevant attributes influencing the evaluation of advantages and disadvantages of field crop robots. Generally, respondents from larger farms focus more on financial benefits from robots and prefer large autonomous tractors. Conversely, small-scale or organic farmers consider environmental benefits of field crop robots relatively more important and favor small robots. Organic farming also positively correlates with the intent to purchase field crop robots within the next five years. More farmers can generally imagine owning small robots as opposed to an autonomous tractor in ten years, but at the same time view autonomous tractors as more suitable for most specified agronomic tasks. Non-purchase options such as contractor services and machinery sharing represent the preferred modes of robot deployment.},
}

F. Görlich, E. Marks, A. Mahlein, K. König, P. Lottes, and C. Stachniss, "UAV-Based Classification of Cercospora Leaf Spot Using RGB Images," Drones, vol. 5, iss. 2, 2021. doi:10.3390/drones5020034
[BibTeX] [PDF]

Plant diseases can impact crop yield. Thus, the detection of plant diseases using sensors that can be mounted on aerial vehicles is in the interest of farmers to support decision-making in integrated pest management and to breeders for selecting tolerant or resistant genotypes. This paper investigated the detection of Cercospora leaf spot (CLS), caused by Cercospora beticola in sugar beet using RGB imagery. We proposed an approach to tackle the CLS detection problem using fully convolutional neural networks, which operate directly on RGB images captured by a UAV. This efficient approach does not require complex multi- or hyper-spectral sensors, but provides reliable results and high sensitivity. We provided a detection pipeline for pixel-wise semantic segmentation of CLS symptoms, healthy vegetation, and background so that our approach can automatically quantify the grade of infestation. We thoroughly evaluated our system using multiple UAV datasets recorded from different sugar beet trial fields. The dataset consisted of a training and a test dataset and originated from different fields. We used it to evaluate our approach under realistic conditions and analyzed its generalization capabilities to unseen environments. The obtained results correlated to visual estimation by human experts significantly. The presented study underlined the potential of high-resolution RGB imaging and convolutional neural networks for plant disease detection under field conditions. The demonstrated procedure is particularly interesting for applications under practical conditions, as no complex and cost-intensive measuring system is required.

@Article{goerlich2021drones,
author = {Görlich, Florian and Marks, Elias and Mahlein, Anne-Katrin and König, Kathrin and Lottes, Philipp and Stachniss, Cyrill},
title = {{UAV-Based Classification of Cercospora Leaf Spot Using RGB Images}},
journal = {Drones},
volume = {5},
year = {2021},
number = {2},
article-number= {34},
url = {https://www.mdpi.com/2504-446X/5/2/34},
issn = {2504-446X},
abstract = {Plant diseases can impact crop yield. Thus, the detection of plant diseases using sensors that can be mounted on aerial vehicles is in the interest of farmers to support decision-making in integrated pest management and to breeders for selecting tolerant or resistant genotypes. This paper investigated the detection of Cercospora leaf spot (CLS), caused by Cercospora beticola in sugar beet using RGB imagery. We proposed an approach to tackle the CLS detection problem using fully convolutional neural networks, which operate directly on RGB images captured by a UAV. This efficient approach does not require complex multi- or hyper-spectral sensors, but provides reliable results and high sensitivity. We provided a detection pipeline for pixel-wise semantic segmentation of CLS symptoms, healthy vegetation, and background so that our approach can automatically quantify the grade of infestation. We thoroughly evaluated our system using multiple UAV datasets recorded from different sugar beet trial fields. The dataset consisted of a training and a test dataset and originated from different fields. We used it to evaluate our approach under realistic conditions and analyzed its generalization capabilities to unseen environments. The obtained results correlated to visual estimation by human experts significantly. The presented study underlined the potential of high-resolution RGB imaging and convolutional neural networks for plant disease detection under field conditions. The demonstrated procedure is particularly interesting for applications under practical conditions, as no complex and cost-intensive measuring system is required.},
doi = {10.3390/drones5020034},
}

C. Latka, T. Heckelei, A. Kuhn, H. Witzke, and L. Kornher, "CAP measures towards environmental sustainability—Trade opportunities for Africa?," Q Open, vol. 1, iss. 1, 2021. doi:10.1093/qopen/qoab003
[BibTeX] [PDF]

{Environmental sustainability is a core aspect of the proposed future EU Common Agricultural Policy (CAP). Policy changes must not compromise socioeconomic development in low-income countries, whereas the extensification of EU agriculture may also create trade opportunities abroad. We apply a global agricultural-economic model to assess EU–African trade-related impacts of potential, environmentally motivated CAP changes. Restrictions on livestock density and nitrogen application reveal reduced EU production levels of meat. This lowers the EU's agricultural environmental burden and share in agricultural trade flows to Africa. However, overall food supply in Africa is not projected to deteriorate substantially, as imports from other world regions and increasing domestic production fill the gap. While this weakens the global emission reduction potential, net livestock producers in Africa may benefit from increasing producer prices. How far potentials for domestic production and trade can be used in African regions depends at least partly on their competitiveness vis-á-vis substituting importers.}

@Article{10.1093/qopen/qoab003,
author = {Latka, Catharina and Heckelei, Thomas and Kuhn, Arnim and Witzke, Heinz-Peter and Kornher, Lukas},
title = "{CAP measures towards environmental sustainability—Trade opportunities for Africa?}",
journal = {Q Open},
volume = {1},
number = {1},
year = {2021},
month = {03},
abstract = "{Environmental sustainability is a core aspect of the proposed future EU Common Agricultural Policy (CAP). Policy changes must not compromise socioeconomic development in low-income countries, whereas the extensification of EU agriculture may also create trade opportunities abroad. We apply a global agricultural-economic model to assess EU–African trade-related impacts of potential, environmentally motivated CAP changes. Restrictions on livestock density and nitrogen application reveal reduced EU production levels of meat. This lowers the EU's agricultural environmental burden and share in agricultural trade flows to Africa. However, overall food supply in Africa is not projected to deteriorate substantially, as imports from other world regions and increasing domestic production fill the gap. While this weakens the global emission reduction potential, net livestock producers in Africa may benefit from increasing producer prices. How far potentials for domestic production and trade can be used in African regions depends at least partly on their competitiveness vis-á-vis substituting importers.}",
issn = {2633-9048},
doi = {10.1093/qopen/qoab003},
note = {qoab003},
url = {https://academic.oup.com/qopen/article/1/1/qoab003/6169735},
}

A. Haupenthal, M. Brax, J. Bentz, H. F. Jungkunst, K. Schützenmeister, and E. Kroener, "Plants control soil gas exchanges possibly via mucilage," Journal of Plant Nutrition and Soil Science, 2021. doi:10.1002/jpln.202000496
[BibTeX] [PDF] [Video]

Abstract Background: Gaseous matter exchanges in soil are determined by the connectivity of the pore system which is easily clogged by fresh root exudates. However, it remains unclear how a hydrogel (e.g., mucilage) affects soil pore tortuosity and gas diffusion properties when drying. Aims: The aim of this viewpoint study is to extend the understanding of gas exchange processes in the rhizosphere by (a) relating it to the patterns formed by drying mucilage within pore space and (b) to give a concept of the effect of drying mucilage on soil gas diffusivity using the combination of experimental evidence and simulations. Methods: To describe the effect of mucilage on soil gas exchanges, we performed gas diffusion experiments on dry soil–mucilage samples and took images of glass beads mixed with mucilage to visualize the formation of mucilage after drying, using Environmental Scanning Electron Microscopy. Finally, we set up simulations to characterize the geometric distribution of mucilage within soil during the drying process. Results: Experiments of gas diffusion show that mucilage decreases gas diffusion coefficient in dry soil without significantly altering bulk density and porosity. Electron microscopy indicates that during drying mucilage forms filaments and interconnected structures throughout the pore space reducing gas phase connectivity. The evolution of these geometric structures is explained via pore scale modelling based on identifying the elastic strength of rhizodeposition during soil drying. Conclusion: Our results suggest that releasing mucilage may be a plant adaption strategy to actively alter gas diffusion in soil.

@Article{https://doi.org/10.1002/jpln.202000496,
author = {Haupenthal, Adrian and Brax, Mathilde and Bentz, Jonas and Jungkunst, Hermann F. and Schützenmeister, Klaus and Kroener, Eva},
title = {Plants control soil gas exchanges possibly via mucilage},
journal = {Journal of Plant Nutrition and Soil Science},
keywords = {gas diffusion coefficient, liquid bridges, mucilage, pore connectivity, pore scale simulation, respiration, rhizosphere},
doi = {10.1002/jpln.202000496},
year = {2021},
videourl  = {https://www.youtube.com/watch?v=Q-EuTs_-VeY},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/jpln.202000496},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/jpln.202000496},
abstract = {Abstract Background: Gaseous matter exchanges in soil are determined by the connectivity of the pore system which is easily clogged by fresh root exudates. However, it remains unclear how a hydrogel (e.g., mucilage) affects soil pore tortuosity and gas diffusion properties when drying. Aims: The aim of this viewpoint study is to extend the understanding of gas exchange processes in the rhizosphere by (a) relating it to the patterns formed by drying mucilage within pore space and (b) to give a concept of the effect of drying mucilage on soil gas diffusivity using the combination of experimental evidence and simulations. Methods: To describe the effect of mucilage on soil gas exchanges, we performed gas diffusion experiments on dry soil–mucilage samples and took images of glass beads mixed with mucilage to visualize the formation of mucilage after drying, using Environmental Scanning Electron Microscopy. Finally, we set up simulations to characterize the geometric distribution of mucilage within soil during the drying process. Results: Experiments of gas diffusion show that mucilage decreases gas diffusion coefficient in dry soil without significantly altering bulk density and porosity. Electron microscopy indicates that during drying mucilage forms filaments and interconnected structures throughout the pore space reducing gas phase connectivity. The evolution of these geometric structures is explained via pore scale modelling based on identifying the elastic strength of rhizodeposition during soil drying. Conclusion: Our results suggest that releasing mucilage may be a plant adaption strategy to actively alter gas diffusion in soil.},
}

P. Yu, X. He, M. Baer, S. Beirinckx, T. Tian, Y. A. T. Moya, X. Zhang, M. Deichmann, F. P. Frey, V. Bresgen, C. Li, B. S. Razavi, G. Schaaf, N. von Wirén, Z. Su, M. Bucher, K. Tsuda, S. Goormachtig, X. Chen, and F. Hochholdinger, "Plant flavones enrich rhizosphere Oxalobacteraceae to improve maize performance under nitrogen deprivation," Nature Plants, p. 1–19, 2021. doi:10.1038/s41477-021-00897-y
[BibTeX]

{Beneficial interactions between plant roots and rhizosphere microorganisms are pivotal for plant fitness. Nevertheless, the molecular mechanisms controlling the feedback between root architecture and microbial community structure remain elusive in maize. Here, we demonstrate that transcriptomic gradients along the longitudinal root axis associate with specific shifts in rhizosphere microbial diversity. Moreover, we have established that root-derived flavones predominantly promote the enrichment of bacteria of the taxa Oxalobacteraceae in the rhizosphere, which in turn promote maize growth and nitrogen acquisition. Genetic experiments demonstrate that LRT1-mediated lateral root development coordinates the interactions of the root system with flavone-dependent Oxalobacteraceae under nitrogen deprivation. In summary, these experiments reveal the genetic basis of the reciprocal interactions between root architecture and the composition and diversity of specific microbial taxa in the rhizosphere resulting in improved plant performance. These findings may open new avenues towards the breeding of high-yielding and nutrient-efficient crops by exploiting their interaction with beneficial soil microorganisms. The link between rhizosphere microbial community, root architecture and performance in nitrogen-poor soils is comprehensively investigated in maize, and the role of exuded flavone to promote specific beneficial bacterial taxa is characterized.}

@Article{10.1038/s41477-021-00897-y,
year = {2021},
title = {{Plant flavones enrich rhizosphere Oxalobacteraceae to improve maize performance under nitrogen deprivation}},
author = {Yu, Peng and He, Xiaoming and Baer, Marcel and Beirinckx, Stien and Tian, Tian and Moya, Yudelsy A T and Zhang, Xuechen and Deichmann, Marion and Frey, Felix P and Bresgen, Verena and Li, Chunjian and Razavi, Bahar S and Schaaf, Gabriel and Wirén, Nicolaus von and Su, Zhen and Bucher, Marcel and Tsuda, Kenichi and Goormachtig, Sofie and Chen, Xinping and Hochholdinger, Frank},
journal = {Nature Plants},
doi = {10.1038/s41477-021-00897-y},
abstract = {{Beneficial interactions between plant roots and rhizosphere microorganisms are pivotal for plant fitness. Nevertheless, the molecular mechanisms controlling the feedback between root architecture and microbial community structure remain elusive in maize. Here, we demonstrate that transcriptomic gradients along the longitudinal root axis associate with specific shifts in rhizosphere microbial diversity. Moreover, we have established that root-derived flavones predominantly promote the enrichment of bacteria of the taxa Oxalobacteraceae in the rhizosphere, which in turn promote maize growth and nitrogen acquisition. Genetic experiments demonstrate that LRT1-mediated lateral root development coordinates the interactions of the root system with flavone-dependent Oxalobacteraceae under nitrogen deprivation. In summary, these experiments reveal the genetic basis of the reciprocal interactions between root architecture and the composition and diversity of specific microbial taxa in the rhizosphere resulting in improved plant performance. These findings may open new avenues towards the breeding of high-yielding and nutrient-efficient crops by exploiting their interaction with beneficial soil microorganisms. The link between rhizosphere microbial community, root architecture and performance in nitrogen-poor soils is comprehensively investigated in maize, and the role of exuded flavone to promote specific beneficial bacterial taxa is characterized.}},
pages = {1--19},
}

M. Popović, F. Thomas, S. Papatheodorou, N. Funk, T. Vidal-Calleja, and S. Leutenegger, "Volumetric Occupancy Mapping With Probabilistic Depth Completion for Robotic Navigation," IEEE Robotics and Automation Letters, 2021. doi:10.1109/lra.2021.3070308
[BibTeX] [PDF] [Video]

@Article{popovic2021,
author = {Marija Popovi{\'c} and Florian Thomas and Sotiris Papatheodorou and Nils Funk and Teresa Vidal-Calleja and Stefan Leutenegger},
title = {{Volumetric Occupancy Mapping With Probabilistic Depth Completion for Robotic Navigation}},
videourl  = {https://www.youtube.com/watch?v=7n0I8SadAAY},
url={https://arxiv.org/pdf/2012.03023.pdf?trk=public_post_comment-text},
doi={10.1109/lra.2021.3070308},
journal = {{IEEE} Robotics and Automation Letters},
year = {2021},
}

S. Dadshani, B. Mathew, A. Ballvora, A. S. Mason, and J. Léon, "Detection of breeding signatures in wheat using a linkage disequilibrium-corrected mapping approach," Scientific Reports, vol. 11, iss. 1, p. 1–12, 2021. doi:10.1038/s41598-021-85226-1
[BibTeX] [PDF]

@Article{dadshani2021detection,
title = {Detection of breeding signatures in wheat using a linkage disequilibrium-corrected mapping approach},
author = {Dadshani, Said and Mathew, Boby and Ballvora, Agim and Mason, Annaliese S and L{\'e}on, Jens},
journal = {Scientific Reports},
volume = {11},
url={https://www.nature.com/articles/s41598-021-85226-1.pdf},
doi={10.1038/s41598-021-85226-1},
number = {1},
pages = {1--12},
year = {2021},
publisher = {Nature Publishing Group},
}

E. Martinsson and H. Hansson, "Adjusting eco-efficiency to greenhouse gas emissions targets at farm level – The case of Swedish dairy farms," Journal of Environmental Management, vol. 287, p. 112313, 2021. doi:10.1016/j.jenvman.2021.112313
[BibTeX]

The purpose of this paper is to adjust the measure of eco-efficiency to account for specific sustainability targets at farm level. We assess eco-efficiency and adjust the scores according to a target of absolute levels of greenhouse gas (GHG) emissions, using Data Envelopment Analysis (DEA) and data from Swedish dairy farms as an illustrative example. In particular, the Swedish target of net-zero emissions in 2045 and vision of a fossil free economy are used to specify the GHG emission target used for assessing the adjusted eco-efficiency scores. We test for possible factors associated with the adjusted and unadjusted eco-efficiency using OLS-regression analysis. The study is based on data from the farm accounting data network (FADN) in year 2016 and considers the environmental pressures nutrients and contribution to global warming. Adjusted as well as unadjusted eco-efficiency scores suggest that Swedish dairy farms are highly inefficient, and that economic value added could increase by 64% (adj) or 67% (unadj) for conventional farms and by 42% (adj) or 41% (unadj) for organic farms at the same level of environmental pressure. Findings further suggest that adjusting the scores towards absolute levels of GHG emissions increases industry average efficiency. Comparing the unadjusted and adjusted efficiency scores using Spearman rank correlation indicates similar efficiency rankings between the unadjusted and adjusted scores. However, findings also indicate that adjusted and unadjusted eco-efficiency scores are associated with different influencing factors, which lends empirical support to the idea that the two types of efficiency scores are conceptually different. Policy recommendations can be made based on insights from the second stage analysis of possible influencing factors. In particular, adjusted eco-efficiency is associated with higher intensity of farming defined as output per livestock unit. Further, adjusted eco-efficiency is associated with a higher number of livestock units in conventional farms and with lower levels of labour per livestock unit in organic farms.

@Article{martinsson2021112313,
title = {Adjusting eco-efficiency to greenhouse gas emissions targets at farm level – The case of Swedish dairy farms},
journal = {Journal of Environmental Management},
volume = {287},
pages = {112313},
year = {2021},
issn = {0301-4797},
doi = {10.1016/j.jenvman.2021.112313},
author = {Elin Martinsson and Helena Hansson},
keywords = {Common agricultural policy, Eco-efficiency, Livestock farming, Planetary boundaries, Sweden},
abstract = {The purpose of this paper is to adjust the measure of eco-efficiency to account for specific sustainability targets at farm level. We assess eco-efficiency and adjust the scores according to a target of absolute levels of greenhouse gas (GHG) emissions, using Data Envelopment Analysis (DEA) and data from Swedish dairy farms as an illustrative example. In particular, the Swedish target of net-zero emissions in 2045 and vision of a fossil free economy are used to specify the GHG emission target used for assessing the adjusted eco-efficiency scores. We test for possible factors associated with the adjusted and unadjusted eco-efficiency using OLS-regression analysis. The study is based on data from the farm accounting data network (FADN) in year 2016 and considers the environmental pressures nutrients and contribution to global warming. Adjusted as well as unadjusted eco-efficiency scores suggest that Swedish dairy farms are highly inefficient, and that economic value added could increase by 64% (adj) or 67% (unadj) for conventional farms and by 42% (adj) or 41% (unadj) for organic farms at the same level of environmental pressure. Findings further suggest that adjusting the scores towards absolute levels of GHG emissions increases industry average efficiency. Comparing the unadjusted and adjusted efficiency scores using Spearman rank correlation indicates similar efficiency rankings between the unadjusted and adjusted scores. However, findings also indicate that adjusted and unadjusted eco-efficiency scores are associated with different influencing factors, which lends empirical support to the idea that the two types of efficiency scores are conceptually different. Policy recommendations can be made based on insights from the second stage analysis of possible influencing factors. In particular, adjusted eco-efficiency is associated with higher intensity of farming defined as output per livestock unit. Further, adjusted eco-efficiency is associated with a higher number of livestock units in conventional farms and with lower levels of labour per livestock unit in organic farms.},
}

N. Chebrolu, F. Magistri, T. Läbe, and C. Stachniss, "Registration of Spatio-Temporal Point Clouds of Plants for Phenotyping," PLOS ONE, 2021. doi:10.1371/journal.pone.0247243
[BibTeX] [PDF] [Code] [Video]

@Article{chebrolu2021plosone,
author = {N. Chebrolu and F. Magistri and T. L{\"a}be and C. Stachniss},
title = {{Registration of Spatio-Temporal Point Clouds of Plants for Phenotyping}},
journal = {PLOS ONE},
year = {2021},
doi={10.1371/journal.pone.0247243},
codeurl = {https://github.com/PRBonn/4d_plant_registration
},
videourl  = {https://www.youtube.com/watch?v=xJqJE_CuCe8},
url = {https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0247243&type=printable},
}

J. Weyler, A. Milioto, T. Falck, J. Behley, and C. Stachniss, "Joint Plant Instance Detection and Leaf Count Estimation for In-Field Plant Phenotyping," IEEE Robotics and Automation Letters (RA-L), 2021. doi:10.1109/lra.2021.3060712
[BibTeX] [PDF] [Video]

@Article{weyler2021ral,
author = {J. Weyler and A. Milioto and T. Falck and J. Behley and C. Stachniss},
title = {{Joint Plant Instance Detection and Leaf Count Estimation for In-Field Plant Phenotyping}},
journal = {IEEE Robotics and Automation Letters (RA-L)},
year = {2021},
url={https://www.researchgate.net/profile/Jan-Weyler-2/publication/349468993_Joint_Plant_Instance_Detection_and_Leaf_Count_Estimation_for_In-Field_Plant_Phenotyping/links/60b4a368299bf1f6d588867f/Joint-Plant-Instance-Detection-and-Leaf-Count-Estimation-for-In-Field-Plant-Phenotyping.pdf},
doi={10.1109/lra.2021.3060712},
videourl = {https://youtu.be/Is18Rey625I},
}

L. Wiesmann, A. Milioto, X. Chen, C. Stachniss, and J. Behley, "Deep Compression for Dense Point Cloud Maps," IEEE Robotics and Automation Letters (RA-L), 2021. doi:10.1109/lra.2021.3059633
[BibTeX] [PDF] [Code] [Video]

@Article{wiesmann2021ral,
author = {L. Wiesmann and A. Milioto and X. Chen and C. Stachniss and J. Behley},
title = {{Deep Compression for Dense Point Cloud Maps}},
journal = {IEEE Robotics and Automation Letters (RA-L)},
year = {2021},
doi={10.1109/lra.2021.3059633},
codeurl  = {https://github.com/PRBonn/deep-point-map-compression},
videourl  = {https://www.youtube.com/watch?v=fLl9lTlZrI0},
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/wiesmann2021ral.pdf},
}

N. Chebrolu, T. Läbe, O. Vysotska, J. Behley, and C. Stachniss, "Adaptive Robust Kernels for Non-Linear Least Squares Problems," IEEE Robotics and Automation Letters (RA-L), 2021. doi:10.1109/lra.2021.3061331
[BibTeX] [PDF] [Video]

@Article{chebrolu2021ral,
author = {N. Chebrolu and T. L\"{a}be and O. Vysotska and J. Behley and C. Stachniss},
title = {{Adaptive Robust Kernels for Non-Linear Least Squares Problems}},
journal = {IEEE Robotics and Automation Letters (RA-L)},
doi={10.1109/lra.2021.3061331},
year = 2021,
videourl  = {https://www.youtube.com/watch?v=34Zp3ZX0Bnk},
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/chebrolu2021ral.pdf},
}

F. Magistri, N. Chebrolu, J. Behley, and C. Stachniss, "Towards In-Field Phenotyping Exploiting Differentiable Rendering with Self-Consistency Loss," in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2021. doi:10.1109/icra48506.2021.9561356
[BibTeX] [PDF] [Video]

@InProceedings{magistri2021icra,
author = {F. Magistri and N. Chebrolu and J. Behley and C. Stachniss},
title = {{Towards In-Field Phenotyping Exploiting Differentiable Rendering with Self-Consistency Loss}},
booktitle = {Proc. of the IEEE Intl. Conf. on Robotics \& Automation (ICRA)},
year = {2021},
videourl = {https://youtu.be/MF2A4ihY2lE},
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/magistri2021icra.pdf},
doi={10.1109/icra48506.2021.9561356},
}

I. Vizzo, X. Chen, N. Chebrolu, J. Behley, and C. Stachniss, "Poisson Surface Reconstruction for LiDAR Odometry and Mapping," in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2021. doi:10.1109/icra48506.2021.9562069
[BibTeX] [PDF] [Code] [Video]

@InProceedings{vizzo2021icra,
author = {I. Vizzo and X. Chen and N. Chebrolu and J. Behley and C. Stachniss},
title = {{Poisson Surface Reconstruction for LiDAR Odometry and Mapping}},
codeurl = {https://github.com/PRBonn/puma},
videourl  = {https://www.youtube.com/watch?v=7yWtYWaO5Nk},
url={https://www.researchgate.net/profile/Xieyuanli-Chen/publication/350399207_Poisson_Surface_Reconstruction_for_LiDAR_Odometry_and_Mapping/links/605daf9792851cd8ce69c865/Poisson-Surface-Reconstruction-for-LiDAR-Odometry-and-Mapping.pdf},
doi={10.1109/icra48506.2021.9562069},
booktitle = {Proc. of the IEEE Intl. Conf. on Robotics \& Automation (ICRA)},
year = 2021,
}

X. Chen, I. Vizzo, T. Läbe, J. Behley, and C. Stachniss, "Range Image-based LiDAR Localization for Autonomous Vehicles," in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2021. doi:10.1109/icra48506.2021.9561335
[BibTeX] [PDF] [Code] [Video]

@InProceedings{chen2021icra,
author = {X. Chen and I. Vizzo and T. L{\"a}be and J. Behley and C. Stachniss},
title = {{Range Image-based LiDAR Localization for Autonomous Vehicles}},
codeurl = {https://github.com/PRBonn/range-mcl},
videourl  = {https://www.youtube.com/watch?v=2r26PlPmuV4},
url={https://arxiv.org/pdf/2105.12121.pdf},
doi={10.1109/icra48506.2021.9561335},
booktitle = {Proc. of the IEEE Intl. Conf. on Robotics \& Automation (ICRA)},
year = 2021,
}

A. Reinke, X. Chen, and C. Stachniss, "Simple But Effective Redundant Odometry for Autonomous Vehicles," in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2021. doi:10.1109/icra48506.2021.9562023
[BibTeX] [PDF] [Code] [Video]

@InProceedings{reinke2021icra,
title = {{Simple But Effective Redundant Odometry for Autonomous Vehicles}},
author = {A. Reinke and X. Chen and C. Stachniss},
codeurl = {https://github.com/PRBonn/MutiverseOdometry},
videourl  = {https://www.youtube.com/watch?v=zLpnPEyDKfM},
url={https://arxiv.org/pdf/2105.11783.pdf},
doi={10.1109/icra48506.2021.9562023},
booktitle = {Proc. of the IEEE Intl. Conf. on Robotics \& Automation (ICRA)},
year = {2021},
}

J. Behley, A. Milioto, and C. Stachniss, "A Benchmark for LiDAR-based Panoptic Segmentation based on KITTI," in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2021. doi:10.1109/icra48506.2021.9561476
[BibTeX] [PDF]

@InProceedings{behley2021icra,
author = {J. Behley and A. Milioto and C. Stachniss},
title = {{A Benchmark for LiDAR-based Panoptic Segmentation based on KITTI}},
booktitle = {Proc. of the IEEE Intl. Conf. on Robotics \& Automation (ICRA)},
url={https://arxiv.org/pdf/2003.02371.pdf},
doi={10.1109/icra48506.2021.9561476},
year = {2021},
}

C. Carbone, D. Albani, F. Magistri, D. Ognibene, C. Stachniss, G. Kootstra, D. Nardi, and V. Trianni, "Monitoring and Mapping of Crop Fields with UAV Swarms Based on Information Gain," in Proc. of the Intl. Symp. on Distributed Autonomous Robotic Systems (DARS) , 2021. doi:10.1007/978-3-030-92790-5_24
[BibTeX] [PDF] [Code]

@InProceedings{carbone2021dars,
author = {C. Carbone and D. Albani and F. Magistri and D. Ognibene and C. Stachniss and G. Kootstra and D. Nardi and V. Trianni},
codeurl = {https://github.com/CSCarbone07/roma_agroSwarm_accelerated?tab=readme-ov-file},
url={https://arxiv.org/pdf/2203.11766.pdf},
doi={10.1007/978-3-030-92790-5_24},
title = {{Monitoring and Mapping of Crop Fields with UAV Swarms Based on Information Gain}},
booktitle = {Proc. of the Intl. Symp. on Distributed Autonomous Robotic Systems (DARS)},
year = 2021,
}

J. S. Bates, C. Montzka, M. Schmidt, and F. Jonard, "Estimating Canopy Density Parameters Time-Series for Winter Wheat Using UAS Mounted LiDAR," Remote Sensing, vol. 13, iss. 4, 2021. doi:10.3390/rs13040710
[BibTeX] [PDF]

Monitoring of canopy density with related metrics such as leaf area index (LAI) makes a significant contribution to understanding and predicting processes in the soil–plant–atmosphere system and to indicating crop health and potential yield for farm management. Remote sensing methods using optical sensors that rely on spectral reflectance to calculate LAI have become more mainstream due to easy entry and availability. Methods with vegetation indices (VI) based on multispectral reflectance data essentially measure the green area index (GAI) or response to chlorophyll content of the canopy surface and not the entire aboveground biomass that may be present from non-green elements that are key to fully assessing the carbon budget. Methods with light detection and ranging (LiDAR) have started to emerge using gap fraction (GF) to estimate the plant area index (PAI) based on canopy density. These LiDAR methods have the main advantage of being sensitive to both green and non-green plant elements. They have primarily been applied to forest cover with manned airborne LiDAR systems (ALS) and have yet to be used extensively with crops such as winter wheat using LiDAR on unmanned aircraft systems (UAS). This study contributes to a better understanding of the potential of LiDAR as a tool to estimate canopy structure in precision farming. The LiDAR method proved to have a high to moderate correlation in spatial variation to the multispectral method. The LiDAR-derived PAI values closely resemble the SunScan Ceptometer GAI ground measurements taken early in the growing season before major stages of senescence. Later in the growing season, when the canopy density was at its highest, a possible overestimation may have occurred. This was most likely due to the chosen flight parameters not providing the best depictions of canopy density with consideration of the LiDAR’s perspective, as the ground-based destructive measurements provided lower values of PAI. Additionally, a distinction between total LiDAR-derived PAI, multispectral-derived GAI, and brown area index (BAI) is made to show how the active and passive optical sensor methods used in this study can complement each other throughout the growing season.

@Article{rs13040710,
author = {Bates, Jordan Steven and Montzka, Carsten and Schmidt, Marius and Jonard, François},
title = {Estimating Canopy Density Parameters Time-Series for Winter Wheat Using UAS Mounted LiDAR},
journal = {Remote Sensing},
volume = {13},
year = {2021},
number = {4},
article-number= {710},
url = {https://www.mdpi.com/2072-4292/13/4/710},
issn = {2072-4292},
abstract = {Monitoring of canopy density with related metrics such as leaf area index (LAI) makes a significant contribution to understanding and predicting processes in the soil–plant–atmosphere system and to indicating crop health and potential yield for farm management. Remote sensing methods using optical sensors that rely on spectral reflectance to calculate LAI have become more mainstream due to easy entry and availability. Methods with vegetation indices (VI) based on multispectral reflectance data essentially measure the green area index (GAI) or response to chlorophyll content of the canopy surface and not the entire aboveground biomass that may be present from non-green elements that are key to fully assessing the carbon budget. Methods with light detection and ranging (LiDAR) have started to emerge using gap fraction (GF) to estimate the plant area index (PAI) based on canopy density. These LiDAR methods have the main advantage of being sensitive to both green and non-green plant elements. They have primarily been applied to forest cover with manned airborne LiDAR systems (ALS) and have yet to be used extensively with crops such as winter wheat using LiDAR on unmanned aircraft systems (UAS). This study contributes to a better understanding of the potential of LiDAR as a tool to estimate canopy structure in precision farming. The LiDAR method proved to have a high to moderate correlation in spatial variation to the multispectral method. The LiDAR-derived PAI values closely resemble the SunScan Ceptometer GAI ground measurements taken early in the growing season before major stages of senescence. Later in the growing season, when the canopy density was at its highest, a possible overestimation may have occurred. This was most likely due to the chosen flight parameters not providing the best depictions of canopy density with consideration of the LiDAR’s perspective, as the ground-based destructive measurements provided lower values of PAI. Additionally, a distinction between total LiDAR-derived PAI, multispectral-derived GAI, and brown area index (BAI) is made to show how the active and passive optical sensor methods used in this study can complement each other throughout the growing season.},
doi = {10.3390/rs13040710},
}

D. Wallach, T. Palosuo, P. Thorburn, E. Gourdain, S. Asseng, B. Basso, S. Buis, N. Crout, C. Dibari, B. Dumont, R. Ferrise, T. Gaiser, C. Garcia, S. Gayler, A. Ghahramani, Z. Hochman, S. Hoek, G. Hoogenboom, H. Horan, M. Huang, M. Jabloun, Q. Jing, E. Justes, K. C. Kersebaum, A. Klosterhalfen, M. Launay, Q. Luo, B. Maestrini, H. Mielenz, M. Moriondo, H. Nariman Zadeh, J. E. Olesen, A. Poyda, E. Priesack, J. W. M. Pullens, B. Qian, N. Schütze, V. Shelia, A. Souissi, X. Specka, A. K. Srivastava, T. Stella, T. Streck, G. Trombi, E. Wallor, J. Wang, T. K. D. Weber, L. Weihermüller, A. de Wit, T. Wöhling, L. Xiao, C. Zhao, Y. Zhu, and S. J. Seidel, "How well do crop modeling groups predict wheat phenology, given calibration data from the target population?," European Journal of Agronomy, vol. 124, p. 126195, 2021. doi:10.1016/j.eja.2020.126195
[BibTeX]

Predicting phenology is essential for adapting varieties to different environmental conditions and for crop management. Therefore, it is important to evaluate how well different crop modeling groups can predict phenology. Multiple evaluation studies have been previously published, but it is still difficult to generalize the findings from such studies since they often test some specific aspect of extrapolation to new conditions, or do not test on data that is truly independent of the data used for calibration. In this study, we analyzed the prediction of wheat phenology in Northern France under observed weather and current management, which is a problem of practical importance for wheat management. The results of 27 modeling groups are evaluated, where modeling group encompasses model structure, i.e. the model equations, the calibration method and the values of those parameters not affected by calibration. The data for calibration and evaluation are sampled from the same target population, thus extrapolation is limited. The calibration and evaluation data have neither year nor site in common, to guarantee rigorous evaluation of prediction for new weather and sites. The best modeling groups, and also the mean and median of the simulations, have a mean absolute error (MAE) of about 3 days, which is comparable to the measurement error. Almost all models do better than using average number of days or average sum of degree days to predict phenology. On the other hand, there are important differences between modeling groups, due to model structural differences and to differences between groups using the same model structure, which emphasizes that model structure alone does not completely determine prediction accuracy. In addition to providing information for our specific environments and varieties, these results are a useful contribution to a knowledge base of how well modeling groups can predict phenology, when provided with calibration data from the target population.

@article{WALLACH2021126195,
title = {How well do crop modeling groups predict wheat phenology, given calibration data from the target population?},
journal = {European Journal of Agronomy},
volume = {124},
pages = {126195},
year = {2021},
issn = {1161-0301},
doi = {10.1016/j.eja.2020.126195},
author = {Daniel Wallach and Taru Palosuo and Peter Thorburn and Emmanuelle Gourdain and Senthold Asseng and Bruno Basso and Samuel Buis and Neil Crout and Camilla Dibari and Benjamin Dumont and Roberto Ferrise and Thomas Gaiser and Cécile Garcia and Sebastian Gayler and Afshin Ghahramani and Zvi Hochman and Steven Hoek and Gerrit Hoogenboom and Heidi Horan and Mingxia Huang and Mohamed Jabloun and Qi Jing and Eric Justes and Kurt Christian Kersebaum and Anne Klosterhalfen and Marie Launay and Qunying Luo and Bernardo Maestrini and Henrike Mielenz and Marco Moriondo and Hasti {Nariman Zadeh} and Jørgen Eivind Olesen and Arne Poyda and Eckart Priesack and Johannes Wilhelmus Maria Pullens and Budong Qian and Niels Schütze and Vakhtang Shelia and Amir Souissi and Xenia Specka and Amit Kumar Srivastava and Tommaso Stella and Thilo Streck and Giacomo Trombi and Evelyn Wallor and Jing Wang and Tobias K.D. Weber and Lutz Weihermüller and Allard {de Wit} and Thomas Wöhling and Liujun Xiao and Chuang Zhao and Yan Zhu and Sabine J. Seidel},
keywords = {Crop model, Phenology prediction, Model evaluation, Wheat},
abstract = {Predicting phenology is essential for adapting varieties to different environmental conditions and for crop management. Therefore, it is important to evaluate how well different crop modeling groups can predict phenology. Multiple evaluation studies have been previously published, but it is still difficult to generalize the findings from such studies since they often test some specific aspect of extrapolation to new conditions, or do not test on data that is truly independent of the data used for calibration. In this study, we analyzed the prediction of wheat phenology in Northern France under observed weather and current management, which is a problem of practical importance for wheat management. The results of 27 modeling groups are evaluated, where modeling group encompasses model structure, i.e. the model equations, the calibration method and the values of those parameters not affected by calibration. The data for calibration and evaluation are sampled from the same target population, thus extrapolation is limited. The calibration and evaluation data have neither year nor site in common, to guarantee rigorous evaluation of prediction for new weather and sites. The best modeling groups, and also the mean and median of the simulations, have a mean absolute error (MAE) of about 3 days, which is comparable to the measurement error. Almost all models do better than using average number of days or average sum of degree days to predict phenology. On the other hand, there are important differences between modeling groups, due to model structural differences and to differences between groups using the same model structure, which emphasizes that model structure alone does not completely determine prediction accuracy. In addition to providing information for our specific environments and varieties, these results are a useful contribution to a knowledge base of how well modeling groups can predict phenology, when provided with calibration data from the target population.}
}

L. Shang, T. Heckelei, M. K. Gerullis, J. Börner, and S. Rasch, "Adoption and diffusion of digital farming technologies - integrating farm-level evidence and system interaction," Agricultural Systems, vol. 190, p. 103074, 2021. doi:10.1016/j.agsy.2021.103074
[BibTeX] [PDF] [Video]

@Article{shang2021103074,
title = {Adoption and diffusion of digital farming technologies - integrating farm-level evidence and system interaction},
journal = {Agricultural Systems},
volume = {190},
pages = {103074},
year = {2021},
issn = {0308-521X},
doi = {10.1016/j.agsy.2021.103074},
videourl  = {https://www.youtube.com/watch?v=xMUyh3H92Qs},
url = {https://www.sciencedirect.com/science/article/pii/S0308521X21000275},
author = {Linmei Shang and Thomas Heckelei and Maria K. Gerullis and Jan Börner and Sebastian Rasch},
}

S. Gedicke, A. Bonerath, B. Niedermann, and J. -H. Haunert, "Zoomless Maps: External Labeling Methods for the Interactive Exploration of Dense Point Sets at a Fixed Map Scale," IEEE Transactions on Visualization and Computer Graphics, vol. 27, iss. 2, pp. 1247-1256, 2021. doi:10.1109/TVCG.2020.3030399
[BibTeX] [PDF]

@Article{9222088,
author = {S. {Gedicke} and A. {Bonerath} and B. {Niedermann} and J. -H. {Haunert}},
journal = {IEEE Transactions on Visualization and Computer Graphics},
title = {Zoomless Maps: External Labeling Methods for the Interactive Exploration of Dense Point Sets at a Fixed Map Scale},
year = {2021},
volume = {27},
url={https://www.phenorob.de/wp-content/uploads/2024/08/Haunert_zoomless_maps.pdf},
number = {2},
pages = {1247-1256},
doi = {10.1109/TVCG.2020.3030399},
}

S. Hadir, T. Gaiser, H. Hüging, M. Athmann, D. Pfarr, R. Kemper, F. Ewert, and S. Seidel, "Sugar Beet Shoot and Root Phenotypic Plasticity to Nitrogen, Phosphorus, Potassium and Lime Omission," Agriculture, vol. 11, iss. 1, 2021. doi:10.3390/agriculture11010021
[BibTeX] [PDF] [Video]

In low input agriculture, a thorough understanding of the plant-nutrient interactions plays a central role. This study aims to investigate the effects of nitrogen (N), phosphorus (P), and potassium (K) and liming omission on shoot growth as well as on topsoil root biomass, growth and morphology (tuber and fibrous roots) of sugar beet grown under field conditions at the Dikopshof long-term fertilizer experiment (Germany). Classical shoot observation methods were combined with root morphology and link measurements using an image analysis program. Omission of the nutrients N, P and K as well as of liming led to a significant decrease in shoot growth. Tuber yield was lowest for the unfertilized and the K omission treatment. The root shoot ratio was highest in the N deficient treatment. In the K omission treatment, a strategic change from a less herringbone root type (early stage) to a more herringbone root type (late stage), which is more efficient for the acquisition of mobile nutrients, was observed. By contrast, a change from a more herringbone (early stage) to a less herringbone root type (late stage) which is less expensive to produce and maintain was observed in the unfertilized treatment. We conclude that sugar beet alters its root morphology as a nutrient acquisition strategy.

@Article{agriculture11010021,
author = {Hadir, Sofia and Gaiser, Thomas and Hüging, Hubert and Athmann, Miriam and Pfarr, Daniel and Kemper, Roman and Ewert, Frank and Seidel, Sabine},
title = {Sugar Beet Shoot and Root Phenotypic Plasticity to Nitrogen, Phosphorus, Potassium and Lime Omission},
journal = {Agriculture},
volume = {11},
year = {2021},
number = {1},
article-number= {21},
Videourl  = {https://www.youtube.com/watch?v=yGN08PbQEUU},
url = {https://www.mdpi.com/2077-0472/11/1/21},
issn = {2077-0472},
abstract = {In low input agriculture, a thorough understanding of the plant-nutrient interactions plays a central role. This study aims to investigate the effects of nitrogen (N), phosphorus (P), and potassium (K) and liming omission on shoot growth as well as on topsoil root biomass, growth and morphology (tuber and fibrous roots) of sugar beet grown under field conditions at the Dikopshof long-term fertilizer experiment (Germany). Classical shoot observation methods were combined with root morphology and link measurements using an image analysis program. Omission of the nutrients N, P and K as well as of liming led to a significant decrease in shoot growth. Tuber yield was lowest for the unfertilized and the K omission treatment. The root shoot ratio was highest in the N deficient treatment. In the K omission treatment, a strategic change from a less herringbone root type (early stage) to a more herringbone root type (late stage), which is more efficient for the acquisition of mobile nutrients, was observed. By contrast, a change from a more herringbone (early stage) to a less herringbone root type (late stage) which is less expensive to produce and maintain was observed in the unfertilized treatment. We conclude that sugar beet alters its root morphology as a nutrient acquisition strategy.},
doi = {10.3390/agriculture11010021},
}

D. Wallach, T. Palosuo, P. Thorburn, Z. Hochman, F. Andrianasolo, S. Asseng, B. Basso, S. Buis, N. Crout, B. Dumont, R. Ferrise, T. Gaiser, S. Gayler, S. Hireman, S. Hoek, H. Horan, G. Hoogenboom, M. Huang, M. Jabloun, P. -E. Jasson, Q. Jing, E. Justes, C. K. Kersebaum, M. Launay, E. Lewan, Q. Luo, B. Maestrini, M. Moriondo, G. Padovan, J. E. Olesen, A. Poyda, E. Priesack, Q. B. Pullens J.W.M, N. Schütze, V. Shelia, A. Souissi, X. Specka, A. K. Srivastava, T. Stella, T. Streck, G. Trombi, E. Wallor, J. Wang, T. H. D. Weber, L. Weihermüller, A. de Wit, T. Wöhling, L. Xiao, C. Zhao, Y. Zhu, and S. J. Seidel, "Multi-model evaluation of phenology prediction for wheat in Australia," Agricultural and Forest Meteorology, vol. 124, pp. 298-299, 2021. doi:10.1016/j.agrformet.2020.108289
[BibTeX] [PDF]

@Article{wallach2021agrformet,
title = "Multi-model evaluation of phenology prediction for wheat in Australia",
journal = "Agricultural and Forest Meteorology",
volume = "124",
pages = "298-299",
year = "2021",
issn = "1161-0301",
url={https://hal.inrae.fr/hal-03119039},
doi = "10.1016/j.agrformet.2020.108289",
author = {Wallach, D. and Palosuo, T. and Thorburn, P. and Hochman, Z. and Andrianasolo, F. and Asseng, S. and Basso, B. and Buis, S. and Crout, N. and Dumont, B. and Ferrise, R. and Gaiser, T. and Gayler, S. and Hireman, S. and Hoek, S. and Horan, H. and Hoogenboom, G. and Huang, M. and Jabloun, M. and Jasson, P.-E. and Jing, Q. and Justes, E. and Kersebaum, C.K. and Launay, M. and Lewan, E. and Luo, Q. and Maestrini, B. and Moriondo, M. and Padovan, G. and Olesen, J.E. and Poyda, A. and Priesack, E. and Pullens, J.W.M, Qian, B. and Schütze, N. and Shelia, V. and Souissi, A. and Specka, X. and Srivastava, A.K. and Stella, T. and Streck, T. and Trombi, G. and Wallor, E. and Wang, J. and Weber, T.H.D. and Weihermüller, L. and de Wit, A. and Wöhling, T. and Xiao, L. and Zhao, C. and Zhu, Y. and Seidel, S. J.},
}

C. Latka, M. Kuiper, S. Frank, T. Heckelei, P. Havlík, H. Witzke, A. Leip, H. D. Cui, A. Kuijsten, J. M. Geleijnse, and M. van Dijk, "Paying the price for environmentally sustainable and healthy EU diets," Global Food Security, vol. 28, p. 100437, 2021. doi:10.1016/j.gfs.2020.100437
[BibTeX] [PDF]

We review consumer-side interventions and their effectiveness to support a transition to healthier and more environmentally sustainable diets and identify taxes/subsidies as relevant instruments. To quantify the scope of necessary tax levels to achieve dietary recommendations on EU average, we apply three established economic models. Our business-as-usual food intake projections stress the need for policy intervention to resolve continued divergence from nutrition guidelines. Our findings suggest that food group specific taxes are effective in reaching nutrition and environmental sustainability targets. However, considerable tax levels are required to achieve the targeted consumption shifts, inducing a discussion about alternative policy designs and current model limitations. A coherent policy package is suggested to approach nutrition and sustainability objectives simultaneously.

@Article{latka2021100437,
title = "Paying the price for environmentally sustainable and healthy EU diets",
journal = "Global Food Security",
volume = "28",
pages = "100437",
year = "2021",
issn = "2211-9124",
doi = "10.1016/j.gfs.2020.100437",
url = "http://www.sciencedirect.com/science/article/pii/S2211912420300912",
author = "Catharina Latka and Marijke Kuiper and Stefan Frank and Thomas Heckelei and Petr Havlík and Heinz-Peter Witzke and Adrian Leip and Hao David Cui and Anneleen Kuijsten and Johanna M. Geleijnse and Michiel {van Dijk}",
abstract = "We review consumer-side interventions and their effectiveness to support a transition to healthier and more environmentally sustainable diets and identify taxes/subsidies as relevant instruments. To quantify the scope of necessary tax levels to achieve dietary recommendations on EU average, we apply three established economic models. Our business-as-usual food intake projections stress the need for policy intervention to resolve continued divergence from nutrition guidelines. Our findings suggest that food group specific taxes are effective in reaching nutrition and environmental sustainability targets. However, considerable tax levels are required to achieve the targeted consumption shifts, inducing a discussion about alternative policy designs and current model limitations. A coherent policy package is suggested to approach nutrition and sustainability objectives simultaneously.",
}

2020

A. P. Wasson, K. A. Nagel, S. Tracy, and M. Watt, "Beyond digging: noninvasive root and rhizosphere phenotyping," Trends in plant science, vol. 25, iss. 1, p. 119–120, 2020. doi:10.1016/j.tplants.2019.10.011
[BibTeX] [PDF]

@Article{wasson2020beyond,
title = {Beyond digging: noninvasive root and rhizosphere phenotyping},
author = {Wasson, Anton P and Nagel, Kerstin A and Tracy, Saoirse and Watt, Michelle},
journal = {Trends in plant science},
volume = {25},
url={https://www.sciencedirect.com/science/article/pii/S1360138519302808?via%3Dihub },
doi={10.1016/j.tplants.2019.10.011},
number = {1},
pages = {119--120},
year = {2020},
publisher = {Elsevier},
}

F. Seiffarth, T. Horváth, and S. Wrobel, "Maximum margin separations in finite closure systems," in Joint European Conference on Machine Learning and Knowledge Discovery in Databases , 2020, p. 3–18. doi:10.1007/978-3-030-67658-2_1
[BibTeX] [PDF] [Code]

@InProceedings{seiffarth2020maximum,
title = {Maximum margin separations in finite closure systems},
author = {Seiffarth, Florian and Horv{\'a}th, Tam{\'a}s and Wrobel, Stefan},
booktitle = {Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
codeurl = {https://github.com/fseiffarth/MaxMarginSeparations},
url = {https://www.mlai.cs.uni-bonn.de/en/paper/seiffarth/maximum_margin_separations.pdf},
doi={10.1007/978-3-030-67658-2_1},
pages = {3--18},
year = {2020},
organization = {Springer},
}

S. Ehosioke, F. Nguyen, S. Rao, T. Kremer, R. E. Placencia-Gomez, J. A. Huisman, A. Kemna, M. Javaux, and S. Garré, "Sensing the electrical properties of roots: A review," Vadose zone journal, 2020. doi:10.1002/vzj2.20082
[BibTeX] [PDF]

@Article{ehosioke2020sensing,
title = {Sensing the electrical properties of roots: A review},
author = {Ehosioke, Solomon and Nguyen, Fr{\'e}d{\'e}ric and Rao, Sathanarayan and Kremer, Thomas and Placencia-Gomez, Roguer Edmundo and Huisman, Johan Alexander and Kemna, Andreas and Javaux, Mathieu and Garr{\'e}, Sarah},
journal = {Vadose zone journal},
url={https://acsess.onlinelibrary.wiley.com/doi/pdfdirect/10.1002/vzj2.20082},
doi={10.1002/vzj2.20082},
year = {2020},
publisher = {Soil Science Society of America},
}

J. S. Bates, C. Montzka, M. Schmidt, and F. Jonard, "Winter Wheat LAI Estimation Using UAV Mounted LiDAR," in 12th GeoMundus Conference (virtual) , 2020. doi:2078.1/241721
[BibTeX] [PDF]

@InProceedings{bates2020winter,
title = {Winter Wheat LAI Estimation Using UAV Mounted LiDAR},
author = {Bates, Jordan Steven and Montzka, Carsten and Schmidt, Marius and Jonard, Fran{\c{c}}ois},
url={https://dial.uclouvain.be/pr/boreal/object/boreal:241721},
doi={2078.1/241721},
booktitle = {12th GeoMundus Conference (virtual)},
year = {2020},
}

F. He, B. Thiele, S. Santhiraraja-Abresch, M. Watt, T. Kraska, A. Ulbrich, and A. J. Kuhn, "Effects of root temperature on the plant growth and food quality of Chinese broccoli (Brassica oleracea var. alboglabra Bailey)," Agronomy, vol. 10, iss. 5, p. 702, 2020. doi:10.3390/agronomy10050702
[BibTeX] [PDF]

@Article{he2020effects,
title = {Effects of root temperature on the plant growth and food quality of Chinese broccoli (Brassica oleracea var. alboglabra Bailey)},
author = {He, Fang and Thiele, Bj{\"o}rn and Santhiraraja-Abresch, Sharin and Watt, Michelle and Kraska, Thorsten and Ulbrich, Andreas and Kuhn, Arnd J},
journal = {Agronomy},
volume = {10},
url={https://www.mdpi.com/2073-4395/10/5/702},
doi={10.3390/agronomy10050702},
number = {5},
pages = {702},
year = {2020},
publisher = {Multidisciplinary Digital Publishing Institute},
}

P. Welke, "Efficient Frequent Subgraph Mining in Transactional Databases," in 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA) , 2020, p. 307–314. doi:10.1109/dsaa49011.2020.00044
[BibTeX] [PDF] [Video]

@InProceedings{welke2020efficient,
title = {Efficient Frequent Subgraph Mining in Transactional Databases},
author = {Welke, Pascal},
booktitle = {2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA)},
videourl  = {https://www.youtube.com/watch?v=J71MaQ7YtnM},
url={https://www.researchgate.net/profile/Pascal-Welke/publication/347083628_Efficient_Frequent_Subgraph_Mining_in_Transactional_Databases/links/623afd722116ee2bd8a9fc9f/Efficient-Frequent-Subgraph-Mining-in-Transactional-Databases.pdf},
doi={10.1109/dsaa49011.2020.00044},
pages = {307--314},
year = {2020},
organization = {IEEE},
}

A. Mehler, W. Hemati, P. Welke, M. Konca, and T. Uslu, "Multiple Texts as a Limiting Factor in Online Learning: Quantifying (Dis-)similarities of Knowledge Networks," Frontiers in Education, vol. 5, 2020. doi:10.3389/feduc.2020.562670
[BibTeX] [PDF]

We test the hypothesis that the extent to which one obtains information on a given topic through Wikipedia depends on the language in which it is consulted. Controlling the size factor, we investigate this hypothesis for a number of 25 subject areas. Since Wikipedia is a central part of the web-based information landscape, this indicates a language-related, linguistic bias. The article therefore deals with the question of whether Wikipedia exhibits this kind of linguistic relativity or not. From the perspective of educational science, the article develops a computational model of the information landscape from which multiple texts are drawn as typical input of web-based reading. For this purpose, it develops a hybrid model of intra- and intertextual similarity of different parts of the information landscape and tests this model on the example of 35 languages and corresponding Wikipedias. In the way it measures the similarities of hypertexts, the article goes beyond existing approaches by examining their structural and semantic aspects intra- and intertextually. In this way it builds a bridge between reading research, educational science, Wikipedia research and computational linguistics.

@ARTICLE{10.3389/feduc.2020.562670,
AUTHOR={Mehler, Alexander and Hemati, Wahed and Welke, Pascal and Konca, Maxim and Uslu, Tolga},
TITLE={Multiple Texts as a Limiting Factor in Online Learning: Quantifying (Dis-)similarities of Knowledge Networks},
JOURNAL={Frontiers in Education},
VOLUME={5},
YEAR={2020},
URL={https://www.frontiersin.org/articles/10.3389/feduc.2020.562670},
DOI={10.3389/feduc.2020.562670},
ISSN={2504-284X},
ABSTRACT={We test the hypothesis that the extent to which one obtains information on a given topic through Wikipedia depends on the language in which it is consulted. Controlling the size factor, we investigate this hypothesis for a number of 25 subject areas. Since Wikipedia is a central part of the web-based information landscape, this indicates a language-related, linguistic bias. The article therefore deals with the question of whether Wikipedia exhibits this kind of linguistic relativity or not. From the perspective of educational science, the article develops a computational model of the information landscape from which multiple texts are drawn as typical input of web-based reading. For this purpose, it develops a hybrid model of intra- and intertextual similarity of different parts of the information landscape and tests this model on the example of 35 languages and corresponding Wikipedias. In the way it measures the similarities of hypertexts, the article goes beyond existing approaches by examining their structural and semantic aspects intra- and intertextually. In this way it builds a bridge between reading research, educational science, Wikipedia research and computational linguistics.}
}

M. Halstead, S. Denman, C. Fookes, and C. McCool, "Fruit Detection in the Wild: The Impact of Varying Conditions and Cultivar," in 2020 Digital Image Computing: Techniques and Applications (DICTA) , 2020, pp. 1-8. doi:10.1109/DICTA51227.2020.9363407
[BibTeX] [PDF]

@InProceedings{9363407,
author = {Halstead, Michael and Denman, Simon and Fookes, Clinton and McCool, Chris},
booktitle = {2020 Digital Image Computing: Techniques and Applications (DICTA)},
title = {Fruit Detection in the Wild: The Impact of Varying Conditions and Cultivar},
year = {2020},
volume = {},
number = {},
pages = {1-8},
url={https://www.conferences.com.au/wp-content/uploads/2020/09/21_CameraReady.pdf},
doi = {10.1109/DICTA51227.2020.9363407},
}

L. Drees, J. Kusche, and R. Roscher, "Multi-modal deep learning with sentinel-3 observations for the detection of oceanic internal waves," ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. V-2-2020, p. 813–820, 2020. doi:10.5194/isprs-annals-V-2-2020-813-2020
[BibTeX] [PDF] [Video]

@Article{isprs-annals-v-2-2020-813-2020,
author = {Drees, L. and Kusche, J. and Roscher, R.},
title = {Multi-modal deep learning with sentinel-3 observations for the detection of oceanic internal waves},
journal = {ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
volume = {V-2-2020},
year = {2020},
pages = {813--820},
videourl  = {https://www.youtube.com/watch?v=xd7WE42OjLw},
url = {https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-2-2020/813/2020/},
doi = {10.5194/isprs-annals-V-2-2020-813-2020},
}

O. -H. Kwon, J. Tanke, and J. Gall, "Recursive Bayesian Filtering for Multiple Human Pose Tracking from Multiple Cameras," in Asian Conference on Computer Vision (ACCV'20) , 2020. doi:10.1007/978-3-030-69532-3_27
[BibTeX] [PDF] [Code]

@InProceedings{kwon2020accv,
author = {Kwon, O.-H. AND Tanke, J. AND Gall, J.},
title = {{Recursive Bayesian Filtering for Multiple Human Pose Tracking from Multiple Cameras}},
booktitle = {Asian Conference on Computer Vision (ACCV'20)},
year = {2020},
doi={10.1007/978-3-030-69532-3_27},
codeurl  = {https://github.com/ohkwon718/bayesian-mvpose},
url = {https://openaccess.thecvf.com/content/ACCV2020/papers/Kwon_Recursive_Bayesian_Filtering_for_Multiple_Human_Pose_Tracking_from_Multiple_ACCV_2020_paper.pdf},
}

C. Lehnert, C. McCool, I. Sa, and T. Perez, "Performance improvements of a sweet pepper harvesting robot in protected cropping environments," Journal of Field Robotics, vol. 37, iss. 7, pp. 1197-1223, 2020. doi:10.1002/rob.21973
[BibTeX]

Abstract Using robots to harvest sweet peppers in protected cropping environments has remained unsolved despite considerable effort by the research community over several decades. In this paper, we present the robotic harvester, Harvey, designed for sweet peppers in protected cropping environments that achieved a 76.5\% success rate on 68 fruit (within a modified scenario) which improves upon our prior work which achieved 58\% on 24 fruit and related sweet pepper harvesting work which achieved 33\% on 39 fruit (for their best tool in a modified scenario). This improvement was primarily achieved through the introduction of a novel peduncle segmentation system using an efficient deep convolutional neural network, in conjunction with three-dimensional postfiltering to detect the critical cutting location. We benchmark the peduncle segmentation against prior art demonstrating an improvement in performance with a F1 score of 0.564 compared to 0.302. The robotic harvester uses a perception pipeline to detect a target sweet pepper and an appropriate grasp and cutting pose used to determine the trajectory of a multimodal harvesting tool to grasp the sweet pepper and cut it from the plant. A novel decoupling mechanism enables the gripping and cutting operations to be performed independently. We perform an in-depth analysis of the full robotic harvesting system to highlight bottlenecks and failure points that future work could address.

@Article{https://doi.org/10.1002/rob.21973,
author = {Lehnert, Chris and McCool, Chris and Sa, Inkyu and Perez, Tristan},
title = {Performance improvements of a sweet pepper harvesting robot in protected cropping environments},
journal = {Journal of Field Robotics},
volume = {37},
number = {7},
pages = {1197-1223},
keywords = {agriculture, grasping, manipulators, perception, robotic harvesting},
doi = {10.1002/rob.21973},
abstract = {Abstract Using robots to harvest sweet peppers in protected cropping environments has remained unsolved despite considerable effort by the research community over several decades. In this paper, we present the robotic harvester, Harvey, designed for sweet peppers in protected cropping environments that achieved a 76.5\% success rate on 68 fruit (within a modified scenario) which improves upon our prior work which achieved 58\% on 24 fruit and related sweet pepper harvesting work which achieved 33\% on 39 fruit (for their best tool in a modified scenario). This improvement was primarily achieved through the introduction of a novel peduncle segmentation system using an efficient deep convolutional neural network, in conjunction with three-dimensional postfiltering to detect the critical cutting location. We benchmark the peduncle segmentation against prior art demonstrating an improvement in performance with a F1 score of 0.564 compared to 0.302. The robotic harvester uses a perception pipeline to detect a target sweet pepper and an appropriate grasp and cutting pose used to determine the trajectory of a multimodal harvesting tool to grasp the sweet pepper and cut it from the plant. A novel decoupling mechanism enables the gripping and cutting operations to be performed independently. We perform an in-depth analysis of the full robotic harvesting system to highlight bottlenecks and failure points that future work could address.},
year = {2020},
}

H. E. Ahrends, S. Siebert, E. E. Rezaei, S. J. Seidel, H. Hüging, F. Ewert, T. Döring, V. Rueda-Ayala, W. Eugster, and T. Gaiser, "Nutrient supply affects the yield stability of major European crops—a 50 year study," Environmental Research Letters, vol. 16, iss. 1, p. 14003, 2020. doi:10.1088/1748-9326/abc849
[BibTeX] [PDF]

Yield stability is important for food security and a sustainable crop production, especially under changing climatic conditions. It is well known that the variability of yields is linked to changes in meteorological conditions. However, little is known about the long-term effects of agronomic management strategies, such as the supply of important nutrients. We analysed the stability of four major European crops grown between 1955 and 2008 at a long-term fertilization experiment located in Germany. Six fertilizer treatments ranged from no fertilization over the omission of individual macronutrients to complete mineral fertilization with all major macronutrients (nitrogen, phosphorus, potassium and calcium). Yield stability was estimated for each crop × treatment combination using the relative yield deviation in each year from the corresponding (nonlinear) trend value (relative yield anomalies (RYA)). Stability was lowest for potato, followed by sugar beet and winter wheat and highest for winter rye. Stability was highest when soils had received all nutrients with the standard deviation of RYA being two to three times lower than for unfertilized plots. The omission of nitrogen and potassium was associated with a decrease in yield stability and a decrease in the number of simultaneous positive and negative yield anomalies among treatments. Especially in root crops nutrient supply strongly influenced both annual yield anomalies and changes in anomalies over time. During the second half of the observation period yield stability decreased for sugar beet and increased for winter wheat. Potato yields were more stable during the second period, but only under complete nutrient supply. The critical role of potassium supply for yield stability suggests potential links to changes in the water balance during the last decades. Results demonstrate the need to explicitly consider the response of crops to long-term nutrient supply for understanding and predicting changes in yield stability.

@Article{ahrends_2020,
doi = {10.1088/1748-9326/abc849},
url = {https://doi.org/10.1088/1748-9326/abc849},
year = 2020,
month = {dec},
publisher = {{IOP} Publishing},
volume = {16},
number = {1},
pages = {014003},
author = {Hella Ellen Ahrends and Stefan Siebert and Ehsan Eyshi Rezaei and Sabine Julia Seidel and Hubert Hüging and Frank Ewert and Thomas Döring and Victor Rueda-Ayala and Werner Eugster and Thomas Gaiser},
title = {Nutrient supply affects the yield stability of major European crops{\textemdash}a 50 year study},
journal = {Environmental Research Letters},
abstract = {Yield stability is important for food security and a sustainable crop production, especially under changing climatic conditions. It is well known that the variability of yields is linked to changes in meteorological conditions. However, little is known about the long-term effects of agronomic management strategies, such as the supply of important nutrients. We analysed the stability of four major European crops grown between 1955 and 2008 at a long-term fertilization experiment located in Germany. Six fertilizer treatments ranged from no fertilization over the omission of individual macronutrients to complete mineral fertilization with all major macronutrients (nitrogen, phosphorus, potassium and calcium). Yield stability was estimated for each crop × treatment combination using the relative yield deviation in each year from the corresponding (nonlinear) trend value (relative yield anomalies (RYA)). Stability was lowest for potato, followed by sugar beet and winter wheat and highest for winter rye. Stability was highest when soils had received all nutrients with the standard deviation of RYA being two to three times lower than for unfertilized plots. The omission of nitrogen and potassium was associated with a decrease in yield stability and a decrease in the number of simultaneous positive and negative yield anomalies among treatments. Especially in root crops nutrient supply strongly influenced both annual yield anomalies and changes in anomalies over time. During the second half of the observation period yield stability decreased for sugar beet and increased for winter wheat. Potato yields were more stable during the second period, but only under complete nutrient supply. The critical role of potassium supply for yield stability suggests potential links to changes in the water balance during the last decades. Results demonstrate the need to explicitly consider the response of crops to long-term nutrient supply for understanding and predicting changes in yield stability.},
}

T. Boas, H. Bogena, T. Grünwald, B. Heinesch, D. Ryu, M. Schmidt, H. Vereecken, A. Western, and H. -J. Hendricks-Franssen, "Improving the representation of cropland sites in the Community Land Model (CLM) version 5.0," Geoscientific Model Development Discussions, vol. 2020, p. 1–37, 2020. doi:10.5194/gmd-2020-241
[BibTeX] [PDF]

@Article{gmd-2020-241,
author = {Boas, T. and Bogena, H. and Gr\"unwald, T. and Heinesch, B. and Ryu, D. and Schmidt, M. and Vereecken, H. and Western, A. and Hendricks-Franssen, H.-J.},
title = {Improving the representation of cropland sites in the Community Land Model (CLM) version 5.0},
journal = {Geoscientific Model Development Discussions},
volume = {2020},
year = {2020},
pages = {1--37},
url = {https://www.phenorob.de/wp-content/uploads/2024/08/Boas_etal_2021_GMD.pdf},
doi = {10.5194/gmd-2020-241},
}

P. Welke, F. Seiffarth, M. Kamp, and S. Wrobel, "HOPS: Probabilistic Subtree Mining for Small and Large Graphs," in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , New York, NY, USA, 2020, p. 1275–1284. doi:10.1145/3394486.3403180
[BibTeX] [PDF] [Code] [Video]

Frequent subgraph mining, i.e., the identification of relevant patterns in graph databases, is a well-known data mining problem with high practical relevance, since next to summarizing the data, the resulting patterns can also be used to define powerful domain-specific similarity functions for prediction. In recent years, significant progress has been made towards subgraph mining algorithms that scale to complex graphs by focusing on tree patterns and probabilistically allowing a small amount of incompleteness in the result. Nonetheless, the complexity of the pattern matching component used for deciding subtree isomorphism on arbitrary graphs has significantly limited the scalability of existing approaches. In this paper, we adapt sampling techniques from mathematical combinatorics to the problem of probabilistic subtree mining in arbitrary databases of many small to medium-size graphs or a single large graph. By restricting on tree patterns, we provide an algorithm that approximately counts or decides subtree isomorphism for arbitrary transaction graphs in sub-linear time with one-sided error. Our empirical evaluation on a range of benchmark graph datasets shows that the novel algorithm substantially outperforms state-of-the-art approaches both in the task of approximate counting of embeddings in single large graphs and in probabilistic frequent subtree mining in large databases of small to medium sized graphs.

@InProceedings{10.1145/3394486.3403180,
author = {Welke, Pascal and Seiffarth, Florian and Kamp, Michael and Wrobel, Stefan},
title = {HOPS: Probabilistic Subtree Mining for Small and Large Graphs},
year = {2020},
isbn = {9781450379984},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
codeurl  = {https://github.com/pwelke/hops},
videourl  = {https://www.youtube.com/watch?v=409bvVH2YS8},
url = {https://doi.org/10.1145/3394486.3403180},
doi = {10.1145/3394486.3403180},
abstract = {Frequent subgraph mining, i.e., the identification of relevant patterns in graph databases, is a well-known data mining problem with high practical relevance, since next to summarizing the data, the resulting patterns can also be used to define powerful domain-specific similarity functions for prediction. In recent years, significant progress has been made towards subgraph mining algorithms that scale to complex graphs by focusing on tree patterns and probabilistically allowing a small amount of incompleteness in the result. Nonetheless, the complexity of the pattern matching component used for deciding subtree isomorphism on arbitrary graphs has significantly limited the scalability of existing approaches. In this paper, we adapt sampling techniques from mathematical combinatorics to the problem of probabilistic subtree mining in arbitrary databases of many small to medium-size graphs or a single large graph. By restricting on tree patterns, we provide an algorithm that approximately counts or decides subtree isomorphism for arbitrary transaction graphs in sub-linear time with one-sided error. Our empirical evaluation on a range of benchmark graph datasets shows that the novel algorithm substantially outperforms state-of-the-art approaches both in the task of approximate counting of embeddings in single large graphs and in probabilistic frequent subtree mining in large databases of small to medium sized graphs.},
booktitle = {Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining},
pages = {1275–1284},
numpages = {10},
location = {Virtual Event, CA, USA},
series = {KDD '20},
}

A. Bonerath, B. Niedermann, J. Diederich, Y. Orgeig, J. Oehrlein, and J. Haunert, "A Time-Windowed Data Structure for Spatial Density Maps," in Proceedings of the 28th International Conference on Advances in Geographic Information Systems , New York, NY, USA, 2020, p. 15–24. doi:10.1145/3397536.3422242
[BibTeX] [PDF] [Video]

The visualization of spatio-temporal data helps researchers understand global processes such as animal migration. In particular, interactively restricting the data to different time windows reveals new insights into the short-term and long-term changes of the research data. Inspired by this use case, we consider the visualization of point data annotated with time stamps. We pick up classical, grid-based density maps as the underlying visualization technique and enhance them with an efficient data structure for arbitrarily specified time-window queries. The running time of the queries is logarithmic in the total number of points and linear in the number of actually colored cells. In experiments on real-world data we show that the data structure answers time-window queries within milliseconds, which supports the interactive exploration of large point sets. Further, the data structure can be used to visualize additional decision problems, e.g., it can answer whether the sum or maximum of additional weights given with the points exceed a certain threshold. We have defined the data structure general enough to also support multiple thresholds expressed by different colors.

@InProceedings{bhn-twdssdm-20,
abstract = {The visualization of spatio-temporal data helps researchers understand global processes such as animal migration. In particular, interactively restricting the data to different time windows reveals new insights into the short-term and long-term changes of the research data. Inspired by this use case, we consider the visualization of point data annotated with time stamps. We pick up classical, grid-based density maps as the underlying visualization technique and enhance them with an efficient data structure for arbitrarily specified time-window queries. The running time of the queries is logarithmic in the total number of points and linear in the number of actually colored cells. In experiments on real-world data we show that the data structure answers time-window queries within milliseconds, which supports the interactive exploration of large point sets. Further, the data structure can be used to visualize additional decision problems, e.g., it can answer whether the sum or maximum of additional weights given with the points exceed a certain threshold. We have defined the data structure general enough to also support multiple thresholds expressed by different colors.},
address = {New York, NY, USA},
author = {Bonerath, Annika and Niedermann, Benjamin and Diederich, Jim and Orgeig, Yannick and Oehrlein, Johannes and Haunert, Jan-Henrik},
booktitle = {Proceedings of the 28th International Conference on Advances in Geographic Information Systems},
doi = {10.1145/3397536.3422242},
isbn = {9781450380195},
keywords = {density maps, time-windowed data structure, point set},
location = {Seattle, WA, USA},
numpages = {10},
pages = {15–24},
publisher = {Association for Computing Machinery},
series = {SIGSPATIAL '20},
title = {A Time-Windowed Data Structure for Spatial Density Maps},
videourl  = {https://www.youtube.com/watch?v=BWosQQZlNeQ},
url = {https://doi.org/10.1145/3397536.3422242},
year = {2020},
}

M. T. de Moraes, Debiasi H., J. C. Franchini, A. A. Mastroberti, R. Levien, D. Leitner, and A. Schnepf, "Soil compaction impacts soybean root growth in an Oxisol from subtropical Brazil," Soil & Tillage Research, vol. 200, 2020. doi:10.1016/j.still.2020.104611
[BibTeX]

@Article{moraes2020str,
title = "Soil compaction impacts soybean root growth in an Oxisol from subtropical Brazil",
journal = "Soil \& Tillage Research",
volume = "200",
year = "2020",
doi={10.1016/j.still.2020.104611},
author = "de Moraes, M. T. and Debiasi, H., and Franchini, J. C. and Mastroberti, A. A. and Levien, R. and Leitner, D. and Schnepf, A.",
}

N. Chebrolu, T. Läbe, and C. Stachniss, "Spatio-Temporal Non-Rigid Registration of 3D Point Clouds of Plants," in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2020. doi:10.1109/icra40945.2020.9197569
[BibTeX] [PDF] [Video]

@InProceedings{chebrolu2020icra,
title = {Spatio-Temporal Non-Rigid Registration of 3D Point Clouds of Plants},
author = {N. Chebrolu and T. Läbe and C. Stachniss},
booktitle = {Proc. of the IEEE Intl. Conf. on Robotics \& Automation (ICRA)},
year = {2020},
doi={10.1109/icra40945.2020.9197569},
url = {http://www.ipb.uni-bonn.de/pdfs/chebrolu2020icra.pdf},
videourl = {https://www.youtube.com/watch?v=uGkep_aelBc},
}

A. Ahmadi, L. Nardi, N. Chebrolu, and C. Stachniss, "Visual Servoing-based Navigation for Monitoring Row-Crop Fields," in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2020. doi:10.1109/icra40945.2020.9197114
[BibTeX] [PDF] [Code] [Video]

@InProceedings{ahmadi2020icra,
title = {Visual Servoing-based Navigation for Monitoring Row-Crop Fields},
author = {A. Ahmadi and L. Nardi and N. Chebrolu and C. Stachniss},
booktitle = {Proc. of the IEEE Intl. Conf. on Robotics \& Automation (ICRA)},
year = {2020},
doi={10.1109/icra40945.2020.9197114},
url = {http://arxiv.org/pdf/1909.12754},
codeurl = {https://github.com/PRBonn/visual-crop-row-navigation},
videourl = {https://youtu.be/0qg6n4sshHk},
}

X. Wu, S. Aravecchia, P. Lottes, C. Stachniss, and C. Pradalier, "Robotic Weed Control Using Automated Weed and Crop Classification," Journal of Field Robotics, vol. 37, pp. 322-340, 2020. doi:10.1002/rob.21938
[BibTeX] [PDF]

@Article{wu2020jfr,
title = {Robotic Weed Control Using Automated Weed and Crop Classification},
author = {X. Wu and S. Aravecchia and P. Lottes and C. Stachniss and C. Pradalier},
journal = {Journal of Field Robotics},
year = {2020},
volume = {37},
numer = {2},
pages = {322-340},
doi={10.1002/rob.21938},
url = {http://www.ipb.uni-bonn.de/pdfs/wu2020jfr.pdf},
}

L. Nardi and C. Stachniss, "Long-Term Robot Navigation in Indoor Environments Estimating Patterns in Traversability Changes," in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2020. doi:10.1109/icra40945.2020.9197078
[BibTeX] [PDF] [Video]

@InProceedings{nardi2020icra,
title = {Long-Term Robot Navigation in Indoor Environments Estimating Patterns in Traversability Changes},
author = {L. Nardi and C. Stachniss},
booktitle = {Proc. of the IEEE Intl. Conf. on Robotics \& Automation (ICRA)},
year = {2020},
doi={10.1109/icra40945.2020.9197078},
url = {http://arxiv.org/pdf/1909.12733},
videourl = {https://www.youtube.com/watch?v=9lNcA3quzwU},
}

X. Chen, T. Läbe, A. Milioto, T. Röhling, O. Vysotska, A. Haag, J. Behley, and C. Stachniss, "OverlapNet: Loop Closing for LiDAR-based SLAM," in Proceedings of Robotics: Science and Systems (RSS) , 2020. doi:10.15607/rss.2020.xvi.009
[BibTeX] [PDF] [Code] [Video]

@InProceedings{chen2020rss,
author = {X. Chen and T. L\"abe and A. Milioto and T. R\"ohling and O. Vysotska and A. Haag and J. Behley and C. Stachniss},
title = {{OverlapNet: Loop Closing for LiDAR-based SLAM}},
booktitle = {Proceedings of Robotics: Science and Systems (RSS)},
year = {2020},
doi={10.15607/rss.2020.xvi.009},
codeurl = {https://github.com/PRBonn/OverlapNet/},
videourl = {https://youtu.be/YTfliBco6aw},
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/chen2020rss.pdf},
}

A. Milioto, J. Behley, C. McCool, and C. Stachniss, "LiDAR Panoptic Segmentation for Autonomous Driving," in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2020. doi:10.1109/iros45743.2020.9340837
[BibTeX] [PDF] [Video]

@InProceedings{milioto2020iros,
author = {A. Milioto and J. Behley and C. McCool and C. Stachniss},
title = {{LiDAR Panoptic Segmentation for Autonomous Driving}},
booktitle={2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year = {2020},
doi={10.1109/iros45743.2020.9340837},
videourl = {https://www.youtube.com/watch?v=C9CTQSosr9I},
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/milioto2020iros.pdf},
}

S. R. Tracy, K. A. Nagel, J. A. Postma, H. Fassbender, A. Wasson, and M. Watt, "Crop Improvement from Phenotyping Roots: Highlights Reveal Expanding Opportunities," Trends in Plant Science, vol. 25, iss. 1, pp. 105-118, 2020. doi:10.1016/j.tplants.2019.10.015
[BibTeX] [PDF] [Video]

Root systems determine the water and nutrients for photosynthesis and harvested products, underpinning agricultural productivity. We highlight 11 programs that integrated root traits into germplasm for breeding, relying on phenotyping. Progress was successful but slow. Today’s phenotyping technologies will speed up root trait improvement. They combine multiple new alleles in germplasm for target environments, in parallel. Roots and shoots are detected simultaneously and nondestructively, seed to seed measures are automated, and field and laboratory technologies are increasingly linked. Available simulation models can aid all phenotyping decisions. This century will see a shift from single root traits to rhizosphere selections that can be managed dynamically on farms and a shift to phenotype-based improvement to accommodate the dynamic complexity of whole crop systems.

@Article{tracy2020105,
title = "Crop Improvement from Phenotyping Roots: Highlights Reveal Expanding Opportunities",
journal = "Trends in Plant Science",
volume = "25",
number = "1",
pages = "105 - 118",
year = "2020",
issn = "1360-1385",
doi = "10.1016/j.tplants.2019.10.015",
videourl  = "https://www.youtube.com/watch?v=2-V_U7D6gtU",
url = "https://www.cell.com/trends/plant-science/fulltext/S1360-1385(19)30284-5",
author = "Saoirse R. Tracy and Kerstin A. Nagel and Johannes A. Postma and Heike Fassbender and Anton Wasson and Michelle Watt",
keywords = "root architecture, simulation model, combinatorial stresses, rhizosphere, agronomy, soil, breeding, water, climate, imaging",
abstract = "Root systems determine the water and nutrients for photosynthesis and harvested products, underpinning agricultural productivity. We highlight 11 programs that integrated root traits into germplasm for breeding, relying on phenotyping. Progress was successful but slow. Today’s phenotyping technologies will speed up root trait improvement. They combine multiple new alleles in germplasm for target environments, in parallel. Roots and shoots are detected simultaneously and nondestructively, seed to seed measures are automated, and field and laboratory technologies are increasingly linked. Available simulation models can aid all phenotyping decisions. This century will see a shift from single root traits to rhizosphere selections that can be managed dynamically on farms and a shift to phenotype-based improvement to accommodate the dynamic complexity of whole crop systems.",
}

O. Zatsarynna, J. Sawatzky, and J. Gall, "Discovering Latent Classes for Semi-Supervised Semantic Segmentation," in DAGM German Conference on Pattern Recognition (GCPR) , 2020. doi:10.1007/978-3-030-71278-5_15
[BibTeX] [PDF]

@InProceedings{zatsarynna2020gcpr,
author = {Zatsarynna, O. and Sawatzky, J. and Gall, J.},
title = {{Discovering Latent Classes for Semi-Supervised Semantic Segmentation}},
booktitle = {DAGM German Conference on Pattern Recognition (GCPR)},
year = {2020},
doi={10.1007/978-3-030-71278-5_15},
url = {http://pages.iai.uni-bonn.de/gall_juergen/download/jgall_latentclasses_gcpr2020.pdf},
}

W. Amelung, D. Bossio, W. de Vries, I. Kögel-Knabner, J. Lehmann, R. Amundson, R. Bol, C. Collins, R. Lal, J. Leifeld, B. Minasny, G. Pan, K. Paustian, C. Rumpel, J. Sanderman, J. W. van Groenigen, S. Mooney, B. van Wesemael, M. Wander, and A. Chabbi, "Towards a global-scale soil climate mitigation strategy," Nature Communications, vol. 11, 2020. doi:10.1038/s41467-020-18887-7
[BibTeX] [PDF]

{Sustainable soil carbon sequestration practices need to be rapidly scaled up and implemented to contribute to climate change mitigation. We highlight that the major potential for carbon sequestration is in cropland soils, especially those with large yield gaps and/or large historic soil organic carbon losses. The implementation of soil carbon sequestration measures requires a diverse set of options, each adapted to local soil conditions and management opportunities, and accounting for site-specific trade-offs. We propose the establishment of a soil information system containing localised information on soil group, degradation status, crop yield gap, and the associated carbon-sequestration potentials, as well as the provision of incentives and policies to translate management options into region- and soil-specific practices.}

@Article{amelung2020,
author = {W. Amelung and D. Bossio and W. de Vries and I. Kögel-Knabner and J. Lehmann and R. Amundson and R. Bol and C. Collins and R. Lal and J. Leifeld and B. Minasny and G. Pan and K. Paustian and C. Rumpel and J. Sanderman and J. W. van Groenigen and S. Mooney and B. van Wesemael and M. Wander and A. Chabbi},
title = "{Towards a global-scale soil climate mitigation strategy}",
journal = {Nature Communications},
volume = {11},
year = {2020},
month = {10},
abstract = "{Sustainable soil carbon sequestration practices need to be rapidly scaled up and implemented to contribute to climate change mitigation. We highlight that the major potential for carbon sequestration is in cropland soils, especially those with large yield gaps and/or large historic soil organic carbon losses. The implementation of soil carbon sequestration measures requires a diverse set of options, each adapted to local soil conditions and management opportunities, and accounting for site-specific trade-offs. We propose the establishment of a soil information system containing localised information on soil group, degradation status, crop yield gap, and the associated carbon-sequestration potentials, as well as the provision of incentives and policies to translate management options into region- and soil-specific practices.}",
doi = {10.1038/s41467-020-18887-7},
url = {https://doi.org/10.1038/s41467-020-18887-7},
}

H. Webber, G. Lischeid, M. Sommer, R. Finger, C. Nendel, T. Gaiser, and F. Ewert, "No perfect storm for crop yield failure in Germany," Environmental Research Letters, vol. 15, iss. 10, p. 104012, 2020. doi:10.1088/1748-9326/aba2a4
[BibTeX] [PDF]

Large-scale crop yield failures are increasingly associated with food price spikes and food insecurity and are a large source of income risk for farmers. While the evidence linking extreme weather to yield failures is clear, consensus on the broader set of weather drivers and conditions responsible for recent yield failures is lacking. We investigate this for the case of four major crops in Germany over the past 20 years using a combination of machine learning and process-based modelling. Our results confirm that years associated with widespread yield failures across crops were generally associated with severe drought, such as in 2018 and to a lesser extent 2003. However, for years with more localized yield failures and large differences in spatial patterns of yield failures between crops, no single driver or combination of drivers was identified. Relatively large residuals of unexplained variation likely indicate the importance of non-weather related factors, such as management (pest, weed and nutrient management and possible interactions with weather) explaining yield failures. Models to inform adaptation planning at farm, market or policy levels are here suggested to require consideration of cumulative resource capture and use, as well as effects of extreme events, the latter largely missing in process-based models. However, increasingly novel combinations of weather events under climate change may limit the extent to which data driven methods can replace process-based models in risk assessments.

@Article{webber_2020,
doi = {10.1088/1748-9326/aba2a4},
url = {https://doi.org/10.1088%2F1748-9326%2Faba2a4},
year = 2020,
month = {sep},
publisher = {{IOP} Publishing},
volume = {15},
number = {10},
pages = {104012},
author = {Heidi Webber and Gunnar Lischeid and Michael Sommer and Robert Finger and Claas Nendel and Thomas Gaiser and Frank Ewert},
title = {No perfect storm for crop yield failure in Germany},
journal = {Environmental Research Letters},
abstract = {Large-scale crop yield failures are increasingly associated with food price spikes and food insecurity and are a large source of income risk for farmers. While the evidence linking extreme weather to yield failures is clear, consensus on the broader set of weather drivers and conditions responsible for recent yield failures is lacking. We investigate this for the case of four major crops in Germany over the past 20 years using a combination of machine learning and process-based modelling. Our results confirm that years associated with widespread yield failures across crops were generally associated with severe drought, such as in 2018 and to a lesser extent 2003. However, for years with more localized yield failures and large differences in spatial patterns of yield failures between crops, no single driver or combination of drivers was identified. Relatively large residuals of unexplained variation likely indicate the importance of non-weather related factors, such as management (pest, weed and nutrient management and possible interactions with weather) explaining yield failures. Models to inform adaptation planning at farm, market or policy levels are here suggested to require consideration of cumulative resource capture and use, as well as effects of extreme events, the latter largely missing in process-based models. However, increasingly novel combinations of weather events under climate change may limit the extent to which data driven methods can replace process-based models in risk assessments.},
}

J. Yi, L. Krusenbaum, P. Unger, H. Hüging, S. J. Seidel, G. Schaaf, and J. Gall, "Deep Learning for Non-Invasive Diagnosis of Nutrient Deficiencies in Sugar Beet Using RGB Images," Sensors, vol. 12, 2020. doi:10.3390/s20205893
[BibTeX] [PDF] [Video]

{In order to enable timely actions to prevent major losses of crops caused by lack of nutrients and, hence, increase the potential yield throughout the growing season while at the same time prevent excess fertilization with detrimental environmental consequences, early, non-invasive, and on-site detection of nutrient deficiency is required. Current non-invasive methods for assessing the nutrient status of crops deal in most cases with nitrogen (N) deficiency only and optical sensors to diagnose N deficiency, such as chlorophyll meters or canopy reflectance sensors, do not monitor N, but instead measure changes in leaf spectral properties that may or may not be caused by N deficiency. In this work, we study how well nutrient deficiency symptoms can be recognized in RGB images of sugar beets. To this end, we collected the Deep Nutrient Deficiency for Sugar Beet (DND-SB) dataset, which contains 5648 images of sugar beets growing on a long-term fertilizer experiment with nutrient deficiency plots comprising N, phosphorous (P), and potassium (K) deficiency, as well as the omission of liming (Ca), full fertilization, and no fertilization at all. We use the dataset to analyse the performance of five convolutional neural networks for recognizing nutrient deficiency symptoms and discuss their limitations.}

@Article{yi2020deep,
author = {Yi, Jinhui and Krusenbaum, Lukas and Unger, Paula and Hüging, Hubert and Seidel, Sabine J and Schaaf, Gabriel and Gall, Juergen},
title = "{Deep Learning for Non-Invasive Diagnosis of Nutrient Deficiencies in Sugar Beet Using RGB Images}",
journal = {Sensors},
volume = {12},
year = {2020},
month = {10},
abstract = "{In order to enable timely actions to prevent major losses of crops caused by lack of nutrients and, hence, increase the potential yield throughout the growing season while at the same time prevent excess fertilization with detrimental environmental consequences, early, non-invasive, and on-site detection of nutrient deficiency is required. Current non-invasive methods for assessing the nutrient status of crops deal in most cases with nitrogen (N) deficiency only and optical sensors to diagnose N deficiency, such as chlorophyll meters or canopy reflectance sensors, do not monitor N, but instead measure changes in leaf spectral properties that may or may not be caused by N deficiency. In this work, we study how well nutrient deficiency symptoms can be recognized in RGB images of sugar beets. To this end, we collected the Deep Nutrient Deficiency for Sugar Beet (DND-SB) dataset, which contains 5648 images of sugar beets growing on a long-term fertilizer experiment with nutrient deficiency plots comprising N, phosphorous (P), and potassium (K) deficiency, as well as the omission of liming (Ca), full fertilization, and no fertilization at all. We use the dataset to analyse the performance of five convolutional neural networks for recognizing nutrient deficiency symptoms and discuss their limitations.}",
doi = {10.3390/s20205893},
videourl  = {https://www.youtube.com/watch?v=2mjVymjHGCA},
url = {https://doi.org/10.3390/s20205893},
}

B. H. Gebrekidan, T. Heckelei, and S. Rasch, "Characterizing Farmers and Farming System in Kilombero Valley Floodplain, Tanzania," Sustainability, vol. 12, 2020. doi:10.3390/su12177114
[BibTeX] [PDF]

{Recognizing the diversity of farmers is crucial for the success of agricultural, rural, or environmental programs and policies aimed at the sustainable use of natural resources. In this study, based on survey data collected in the Kilombero Valley Floodplain (KVF) in Tanzania, we design a typology of farmers to describe the range of farm types and farming systems systematically, and to understand their livelihood and land use behavior. The KVF is the largest, low-altitude, seasonally-flooded, freshwater wetland in East Africa. Despite its values, KVF is a very fragile ecosystem threatened by current and future human interventions. We apply multivariate statistical analysis (a combination of principal component analysis and cluster analysis) to identify farm groups that are homogenous within and heterogeneous between groups. Three farm types were identified: “Monocrop rice producer”, “Diversifier”, and “Agropastoralist”. Monocrop rice producers are the dominant farm types, accounting for 65 percent of the farm households in the valley, characterized by more than 80 percent of the land allocated to rice, showing strong market participation and high utilization of labor. Diversifiers, on the other hand, allocate more land to maize and vegetables. Agropastoralists account for 7 percent of the surveyed farmers and differ from the other two groups by, on average, larger land ownership, a combination of livestock and crop production, and larger household sizes. This typology represents the diversity of farmers in KVF concerning their land use and livelihood strategy, and will allow to target policy interventions. Besides, it may also inform further research about the diverse landscape of floodplain farming, through the classification and interpretation of different socio-economic positions of farm households.}

@Article{gebrekidan2020,
author = {Gebrekidan, Bisrat Haile and Heckelei, Thomas and Rasch, Sebastian},
title = "{Characterizing Farmers and Farming System in Kilombero Valley Floodplain, Tanzania}",
journal = {Sustainability},
volume = {12},
year = {2020},
month = {08},
abstract = "{Recognizing the diversity of farmers is crucial for the success of agricultural, rural, or environmental programs and policies aimed at the sustainable use of natural resources. In this study, based on survey data collected in the Kilombero Valley Floodplain (KVF) in Tanzania, we design a typology of farmers to describe the range of farm types and farming systems systematically, and to understand their livelihood and land use behavior. The KVF is the largest, low-altitude, seasonally-flooded, freshwater wetland in East Africa. Despite its values, KVF is a very fragile ecosystem threatened by current and future human interventions. We apply multivariate statistical analysis (a combination of principal component analysis and cluster analysis) to identify farm groups that are homogenous within and heterogeneous between groups. Three farm types were identified: “Monocrop rice producer”, “Diversifier”, and “Agropastoralist”. Monocrop rice producers are the dominant farm types, accounting for 65 percent of the farm households in the valley, characterized by more than 80 percent of the land allocated to rice, showing strong market participation and high utilization of labor. Diversifiers, on the other hand, allocate more land to maize and vegetables. Agropastoralists account for 7 percent of the surveyed farmers and differ from the other two groups by, on average, larger land ownership, a combination of livestock and crop production, and larger household sizes. This typology represents the diversity of farmers in KVF concerning their land use and livelihood strategy, and will allow to target policy interventions. Besides, it may also inform further research about the diverse landscape of floodplain farming, through the classification and interpretation of different socio-economic positions of farm households.}",
doi = {10.3390/su12177114},
url = {https://doi.org/10.3390/su12177114},
}

T. Zaenker, F. Verdoja, and V. Kyrki, "Hypermap Mapping Framework and its Application to Autonomous Semantic Exploration," in 2020 IEEE Conference on Multisensor Fusion and Integration , 2020, pp. 133-139. doi:10.1109/mfi49285.2020.9235231
[BibTeX] [PDF] [Video]

@InProceedings{zaenker2020hypermap,
title = {Hypermap Mapping Framework and its Application to Autonomous Semantic Exploration},
author = {Zaenker, Tobias and Verdoja, Francesco and Kyrki, Ville},
booktitle = {2020 IEEE Conference on Multisensor Fusion and Integration},
year = {2020},
doi={10.1109/mfi49285.2020.9235231},
videourl={https://www.youtube.com/watch?v=mdxpBlK4qjk},
url={https://arxiv.org/pdf/1909.09526},
organization = {IEEE},
pages={133-139},
url = {https://arxiv.org/pdf/1909.09526.pdf}
}

C. Gebauer and M. Bennewitz, "Penalized Bootstrapping for Reinforcement Learning in Robot Control," in 2nd International Conference on Machine Learning & Applications , 2020. doi:10.5121/csit.2020.101114
[BibTeX] [PDF]

@InProceedings{gebauer2020penalized,
title = {Penalized Bootstrapping for Reinforcement Learning in Robot Control},
author = {Gebauer, Christopher and Bennewitz, Maren},
booktitle = {2nd International Conference on Machine Learning \& Applications},
year = {2020},
doi={10.5121/csit.2020.101114},
url = {https://www.hrl.uni-bonn.de/papers/gebauer20deeprl-2.pdf/@@display-file/file},
}

P. Regier, L. Gesing, and M. Bennewitz, "Deep Reinforcement Learning for Navigation in Cluttered Environments," in 2nd International Conference on Machine Learning & Applications , 2020. doi:10.5121/csit.2020.101117
[BibTeX] [PDF]

@InProceedings{regier2020deep,
title = {Deep Reinforcement Learning for Navigation in Cluttered Environments},
author = {Regier, Peter and Gesing, Lukas and Bennewitz, Maren},
booktitle = {2nd International Conference on Machine Learning \& Applications},
year = {2020},
doi={10.5121/csit.2020.101117},
url = {https://aircconline.com/csit/papers/vol10/csit101117.pdf},
}

D. Gogoll, P. Lottes, J. Weyler, N. Petrinic, and C. Stachniss, "Unsupervised Domain Adaptation for Transferring Plant Classification Systems to New Field Environments, Crops, and Robots," in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2020. doi:10.1109/iros45743.2020.9341277
[BibTeX] [PDF] [Video]

@InProceedings{gogoll2020iros,
author = {D. Gogoll and P. Lottes and J. Weyler and N. Petrinic and C. Stachniss},
title = {{Unsupervised Domain Adaptation for Transferring Plant Classification Systems to New Field Environments, Crops, and Robots}},
booktitle={2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
doi={10.1109/iros45743.2020.9341277},
year = {2020},
url = {http://www.ipb.uni-bonn.de/pdfs/gogoll2020iros.pdf},
videourl = {https://www.youtube.com/watch?v=6K79Ih6KXTs},
}

F. Magistri, N. Chebrolu, and C. Stachniss, "Segmentation-Based 4D Registration of Plants Point Clouds for Phenotyping," in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2020. doi:10.1109/iros45743.2020.9340918
[BibTeX] [PDF] [Code] [Video]

@InProceedings{magistri2020iros,
author = {F. Magistri and N. Chebrolu and C. Stachniss},
title = {{Segmentation-Based 4D Registration of Plants Point Clouds for Phenotyping}},
booktitle={2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year = {2020},
doi={10.1109/iros45743.2020.9340918},
url = {https://www.ipb.uni-bonn.de/pdfs/magistri2020iros.pdf},
videourl = {https://youtu.be/OV39kb5Nqg8},
Codeurl  = {https://github.com/PRBonn/4d_plant_registration},
}

X. Chen, T. Läbe, L. Nardi, J. Behley, and C. Stachniss, "Learning an Overlap-based Observation Model for 3D LiDAR Localization," in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2020. doi:10.1109/iros45743.2020.9340769
[BibTeX] [PDF] [Code] [Video]

@InProceedings{chen2020iros,
author = {X. Chen and T. L\"abe and L. Nardi and J. Behley and C. Stachniss},
title = {{Learning an Overlap-based Observation Model for 3D LiDAR Localization}},
booktitle={2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year = {2020},
url = {http://www.ipb.uni-bonn.de/pdfs/chen2020iros.pdf},
doi={10.1109/iros45743.2020.9340769},
videourl = {https://www.youtube.com/watch?v=BozPqy_6YcE},
codeurl = {https://github.com/PRBonn/overlap_localization},
}

F. Langer, A. Milioto, A. Haag, J. Behley, and C. Stachniss, "Domain Transfer for Semantic Segmentation of LiDAR Data using Deep Neural Networks," in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2020, pp. 8263-8270. doi:10.1109/IROS45743.2020.9341508
[BibTeX] [PDF] [Code] [Video]

Inferring semantic information towards an understanding of the surrounding environment is crucial for autonomous vehicles to drive safely. Deep learning-based segmentation methods can infer semantic information directly from laser range data, even in the absence of other sensor modalities such as cameras. In this paper, we address improving the generalization capabilities of such deep learning models to range data that was captured using a different sensor and in situations where no labeled data is available for the new sensor setup. Our approach assists the domain transfer of a LiDAR-only semantic segmentation model to a different sensor and environment exploiting existing geometric mapping systems. To this end, we fuse sequential scans in the source dataset into a dense mesh and render semi-synthetic scans that match those of the target sensor setup. Unlike simulation, this approach provides a real-to-real transfer of geometric information and delivers additionally more accurate remission information. We implemented and thoroughly tested our approach by transferring semantic scans between two different real-world datasets with different sensor setups. Our experiments show that we can improve the segmentation performance substantially with zero manual re-labeling. This approach solves the number one feature request since we released our semantic segmentation library LiDAR-bonnetal [18].

@INPROCEEDINGS{9341508,
author={Langer, Ferdinand and Milioto, Andres and Haag, Alexandre and Behley, Jens and Stachniss, Cyrill},
booktitle={2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
title={Domain Transfer for Semantic Segmentation of LiDAR Data using Deep Neural Networks},
year={2020},
volume={},
number={},
pages={8263-8270},
abstract={Inferring semantic information towards an understanding of the surrounding environment is crucial for autonomous vehicles to drive safely. Deep learning-based segmentation methods can infer semantic information directly from laser range data, even in the absence of other sensor modalities such as cameras. In this paper, we address improving the generalization capabilities of such deep learning models to range data that was captured using a different sensor and in situations where no labeled data is available for the new sensor setup. Our approach assists the domain transfer of a LiDAR-only semantic segmentation model to a different sensor and environment exploiting existing geometric mapping systems. To this end, we fuse sequential scans in the source dataset into a dense mesh and render semi-synthetic scans that match those of the target sensor setup. Unlike simulation, this approach provides a real-to-real transfer of geometric information and delivers additionally more accurate remission information. We implemented and thoroughly tested our approach by transferring semantic scans between two different real-world datasets with different sensor setups. Our experiments show that we can improve the segmentation performance substantially with zero manual re-labeling. This approach solves the number one feature request since we released our semantic segmentation library LiDAR-bonnetal [18].},
keywords={},
doi={10.1109/IROS45743.2020.9341508},
ISSN={2153-0866},
month={Oct},
codeurl={https://github.com/PRBonn/lidar_transfer},
videourl={https://www.youtube.com/watch?v=6FNGF4hKBD0},
url={http://www.ipb.uni-bonn.de/pdfs/langer2020iros.pdf
}}

R. Sheikh, A. Milioto, P. Lottes, C. Stachniss, M. Bennewitz, and T. Schultz, "Gradient and Log-based Active Learning for Semantic Segmentation of Crop and Weed for Agricultural Robots," in 2020 IEEE International Conference on Robotics and Automation (ICRA) , 2020, pp. 1350-1356. doi:10.1109/ICRA40945.2020.9196722
[BibTeX] [PDF]

@INPROCEEDINGS{9196722,
author={Sheikh, Rasha and Milioto, Andres and Lottes, Philipp and Stachniss, Cyrill and Bennewitz, Maren and Schultz, Thomas},
booktitle={2020 IEEE International Conference on Robotics and Automation (ICRA)},
title={Gradient and Log-based Active Learning for Semantic Segmentation of Crop and Weed for Agricultural Robots},
year={2020},
volume={},
number={},
pages={1350-1356},
doi={10.1109/ICRA40945.2020.9196722},
url={http://www.ipb.uni-bonn.de/pdfs/sheikh2020icra.pdf}}

B. Heeren, S. Paulus, H. Goldbach, H. Kuhlmann, A. Mahlein, M. Rumpf, and B. Wirth, "Statistical shape analysis of tap roots: a methodological case study on laser scanned sugar beets," BMC Bioinformatics, vol. 21, iss. 1, 2020. doi:10.1186/s12859-020-03654-8
[BibTeX] [PDF]

{The efficient and robust statistical analysis of the shape of plant organs of different cultivars is an important investigation issue in plant breeding and enables a robust cultivar description within the breeding progress. Laserscanning is a highly accurate and high resolution technique to acquire the 3D shape of plant surfaces. The computation of a shape based principal component analysis (PCA) built on concepts from continuum mechanics has proven to be an effective tool for a qualitative and quantitative shape examination.}

@Article{heeren2020,
author = {Heeren, Behrend and Paulus, Stefan and Goldbach, Heiner and Kuhlmann, Heiner and Mahlein, Anne-Katrin and Rumpf, Martin and Wirth, Benedikt},
title = "{Statistical shape analysis of tap roots: a methodological case study on laser scanned sugar beets}",
journal = {BMC Bioinformatics},
volume = {21},
number = {1},
year = {2020},
month = {07},
abstract = "{The efficient and robust statistical analysis of the shape of plant organs of different cultivars is an important investigation issue in plant breeding and enables a robust cultivar description within the breeding progress. Laserscanning is a highly accurate and high resolution technique to acquire the 3D shape of plant surfaces. The computation of a shape based principal component analysis (PCA) built on concepts from continuum mechanics has proven to be an effective tool for a qualitative and quantitative shape examination.}",
issn = {1471-2105},
doi = {10.1186/s12859-020-03654-8},
url = {https://doi.org/10.1186/s12859-020-03654-8},
}

S. Paulus and A. Mahlein, "Technical workflows for hyperspectral plant image assessment and processing on the greenhouse and laboratory scale," GigaScience, vol. 9, iss. 8, 2020. doi:10.1093/gigascience/giaa090
[BibTeX] [PDF]

{The use of hyperspectral cameras is well established in the field of plant phenotyping, especially as a part of high-throughput routines in greenhouses. Nevertheless, the workflows used differ depending on the applied camera, the plants being imaged, the experience of the users, and the measurement set-up.This review describes a general workflow for the assessment and processing of hyperspectral plant data at greenhouse and laboratory scale. Aiming at a detailed description of possible error sources, a comprehensive literature review of possibilities to overcome these errors and influences is provided. The processing of hyperspectral data of plants starting from the hardware sensor calibration, the software processing steps to overcome sensor inaccuracies, and the preparation for machine learning is shown and described in detail. Furthermore, plant traits extracted from spectral hypercubes are categorized to standardize the terms used when describing hyperspectral traits in plant phenotyping. A scientific data perspective is introduced covering information for canopy, single organs, plant development, and also combined traits coming from spectral and 3D measuring devices.This publication provides a structured overview on implementing hyperspectral imaging into biological studies at greenhouse and laboratory scale. Workflows have been categorized to define a trait-level scale according to their metrological level and the processing complexity. A general workflow is shown to outline procedures and requirements to provide fully calibrated data of the highest quality. This is essential for differentiation of the smallest changes from hyperspectral reflectance of plants, to track and trace hyperspectral development as an answer to biotic or abiotic stresses.}

@Article{10.1093/gigascience/giaa090,
author = {Paulus, Stefan and Mahlein, Anne-Katrin},
title = "{Technical workflows for hyperspectral plant image assessment and processing on the greenhouse and laboratory scale}",
journal = {GigaScience},
volume = {9},
number = {8},
year = {2020},
month = {08},
abstract = "{The use of hyperspectral cameras is well established in the field of plant phenotyping, especially as a part of high-throughput routines in greenhouses. Nevertheless, the workflows used differ depending on the applied camera, the plants being imaged, the experience of the users, and the measurement set-up.This review describes a general workflow for the assessment and processing of hyperspectral plant data at greenhouse and laboratory scale. Aiming at a detailed description of possible error sources, a comprehensive literature review of possibilities to overcome these errors and influences is provided. The processing of hyperspectral data of plants starting from the hardware sensor calibration, the software processing steps to overcome sensor inaccuracies, and the preparation for machine learning is shown and described in detail. Furthermore, plant traits extracted from spectral hypercubes are categorized to standardize the terms used when describing hyperspectral traits in plant phenotyping. A scientific data perspective is introduced covering information for canopy, single organs, plant development, and also combined traits coming from spectral and 3D measuring devices.This publication provides a structured overview on implementing hyperspectral imaging into biological studies at greenhouse and laboratory scale. Workflows have been categorized to define a trait-level scale according to their metrological level and the processing complexity. A general workflow is shown to outline procedures and requirements to provide fully calibrated data of the highest quality. This is essential for differentiation of the smallest changes from hyperspectral reflectance of plants, to track and trace hyperspectral development as an answer to biotic or abiotic stresses.}",
issn = {2047-217X},
doi = {10.1093/gigascience/giaa090},
url = {https://doi.org/10.1093/gigascience/giaa090},
note = {giaa090},
eprint = {https://academic.oup.com/gigascience/article-pdf/9/8/giaa090/33667602/giaa090.pdf},
}

P. Schramowski, W. Stammer, S. Teso, A. Brugger, F. Herbert, X. Shao, H. Luigs, A. Mahlein, and K. Kersting, "Making deep neural networks right for the right scientific reasons by interacting with their explanations," Nature Machine Intelligence, vol. 2, 2020. doi:10.1038/s42256-020-0212-3
[BibTeX] [Code]

Deep neural networks have demonstrated excellent performances in many real-world applications. Unfortunately, they may show Clever Hans-like behaviour (making use of confounding factors within datasets) to achieve high performance. In this work we introduce the novel learning setting of explanatory interactive learning and illustrate its benefits on a plant phenotyping research task. Explanatory interactive learning adds the scientist into the training loop, who interactively revises the original model by providing feedback on its explanations. Our experimental results demonstrate that explanatory interactive learning can help to avoid Clever Hans moments in machine learning and encourages (or discourages, if appropriate) trust in the underlying model.

@Article{schramowski2020,
title = "Making deep neural networks right for the right scientific reasons by interacting with their explanations",
journal = "Nature Machine Intelligence",
volume = "2",
year = "2020",
issn = "2522-5839",
codeurl  = "https://codeocean.com/capsule/7818629/tree/v1",
doi = "10.1038/s42256-020-0212-3",
author = "Schramowski, Patrick and Stammer, Wolfgang and Teso, Stefano and Brugger, Anna and Herbert, Franziska and Shao, Xiaoting and Luigs, Hans-Georg and Mahlein, Anne-Katrin and Kersting, Kristian",
abstract = "Deep neural networks have demonstrated excellent performances in many real-world applications. Unfortunately, they may show Clever Hans-like behaviour (making use of confounding factors within datasets) to achieve high performance. In this work we introduce the novel learning setting of explanatory interactive learning and illustrate its benefits on a plant phenotyping research task. Explanatory interactive learning adds the scientist into the training loop, who interactively revises the original model by providing feedback on its explanations. Our experimental results demonstrate that explanatory interactive learning can help to avoid Clever Hans moments in machine learning and encourages (or discourages, if appropriate) trust in the underlying model.",
}

M. Langensiepen, M. A. K. Jansen, A. Wingler, B. Demmig-Adams, W. W. Adams, I. C. Dodd, V. Fotopoulos, R. Snowdon, E. Fenollosa, M. C. De Tullio, G. Buck-Sorlin, and S. Munné-Bosch, "Linking integrative plant physiology with agronomy to sustain future plant production," Environmental and Experimental Botany, vol. 178, p. 104125, 2020. doi:10.1016/j.envexpbot.2020.104125
[BibTeX]

Sustainable production of high-quality food is one of today’s major challenges of agriculture. To achieve this goal, a better understanding of plant physiological processes and a more integrated approach with respect to current agronomical practices are needed. In this review, various examples of cooperation between integrative plant physiology and agronomy are discussed, and this demonstrates the complexity of these interrelations. The examples are meant to stimulate discussions on how both research areas can deliver solutions to avoid looming food crises due to population growth and climate change. In the last decades, unprecedented progress has been made in the understanding of how plants grow and develop in a variety of environments and in response to biotic stresses, but appropriate management and interpretation of the resulting complex datasets remains challenging. After providing an historical overview of integrative plant physiology, we discuss possible avenues of integration, involving advances in integrative plant physiology, to sustain plant production in the current post-omics era. Finally, recommendations are provided on how to practice the transdisciplinary mindset required, emphasising a broader approach to sustainable production of high-quality food in the future, whereby all those who are involved are made partners in knowledge generation processes through transdisciplinary cooperation.

@Article{langensiepen2020104125,
title = "Linking integrative plant physiology with agronomy to sustain future plant production",
journal = "Environmental and Experimental Botany",
volume = "178",
pages = "104125",
year = "2020",
issn = "0098-8472",
doi = "10.1016/j.envexpbot.2020.104125",
author = "Matthias Langensiepen and Marcel A.K. Jansen and Astrid Wingler and Barbara Demmig-Adams and William W. Adams and Ian C. Dodd and Vasileios Fotopoulos and Rod Snowdon and Erola Fenollosa and Mario C. {De Tullio} and Gerhard Buck-Sorlin and Sergi Munné-Bosch",
keywords = "Food production, Molecular plant biology, Plant physiology, Agronomy, Sustainability, Transdisciplinarity",
abstract = "Sustainable production of high-quality food is one of today’s major challenges of agriculture. To achieve this goal, a better understanding of plant physiological processes and a more integrated approach with respect to current agronomical practices are needed. In this review, various examples of cooperation between integrative plant physiology and agronomy are discussed, and this demonstrates the complexity of these interrelations. The examples are meant to stimulate discussions on how both research areas can deliver solutions to avoid looming food crises due to population growth and climate change. In the last decades, unprecedented progress has been made in the understanding of how plants grow and develop in a variety of environments and in response to biotic stresses, but appropriate management and interpretation of the resulting complex datasets remains challenging. After providing an historical overview of integrative plant physiology, we discuss possible avenues of integration, involving advances in integrative plant physiology, to sustain plant production in the current post-omics era. Finally, recommendations are provided on how to practice the transdisciplinary mindset required, emphasising a broader approach to sustainable production of high-quality food in the future, whereby all those who are involved are made partners in knowledge generation processes through transdisciplinary cooperation.",
}

J. Bömer, L. Zabawa, P. Sieren, A. Kicherer, L. Klingbeil, U. Rascher, O. Muller, H. Kuhlmann, and R. Roscher, "Automatic Differentiation of Damaged and Unharmed Grapes Using RGB Images and Convolutional Neural Networks," in Computer Vision – ECCV 2020 Workshops , Cham, 2020, p. 347–359. doi:10.1007/978-3-030-65414-6_24
[BibTeX] [Video]

Knowledge about the damage of grapevine berries in the vineyard is important for breeders and farmers. Damage to berries can be caused for example by mechanical machines during vineyard management, various diseases, parasites or abiotic stress like sun damage. The manual detection of damaged berries in the field is a subjective and labour-intensive task, and automatic detection by machine learning methods is challenging if all variants of damage should be modelled. Our proposed method detects regions of damaged berries in images in an efficient and objective manner using a shallow neural network, where the severeness of the damage is visualized with a heatmap.

@InProceedings{10.1007/978-3-030-65414-6_24,
author="B{\"o}mer, Jonas
and Zabawa, Laura
and Sieren, Philipp
and Kicherer, Anna
and Klingbeil, Lasse
and Rascher, Uwe
and Muller, Onno
and Kuhlmann, Heiner
and Roscher, Ribana",
editor="Bartoli, Adrien
and Fusiello, Andrea",
title="Automatic Differentiation of Damaged and Unharmed Grapes Using RGB Images and Convolutional Neural Networks",
booktitle="Computer Vision -- ECCV 2020 Workshops",
year="2020",
publisher="Springer International Publishing",
address="Cham",
pages="347--359",
videourl="https://www.youtube.com/watch?v=9mIy4dgfq6A",
doi="10.1007/978-3-030-65414-6_24",
abstract="Knowledge about the damage of grapevine berries in the vineyard is important for breeders and farmers. Damage to berries can be caused for example by mechanical machines during vineyard management, various diseases, parasites or abiotic stress like sun damage. The manual detection of damaged berries in the field is a subjective and labour-intensive task, and automatic detection by machine learning methods is challenging if all variants of damage should be modelled. Our proposed method detects regions of damaged berries in images in an efficient and objective manner using a shallow neural network, where the severeness of the damage is visualized with a heatmap.",
isbn="978-3-030-65414-6"
}

J. Quenzel, R. A. Rosu, T. Läbe, C. Stachniss, and S. Behnke, "Beyond Photometric Consistency: Gradient-based Dissimilarity for Improving Visual Odometry and Stereo Matching," in 2020 IEEE International Conference on Robotics and Automation (ICRA) , 2020, pp. 272-278. doi:10.1109/ICRA40945.2020.9197483
[BibTeX] [PDF] [Video]

@INPROCEEDINGS{9197483,
author={Quenzel, Jan and Rosu, Radu Alexandru and Läbe, Thomas and Stachniss, Cyrill and Behnke, Sven},
booktitle={2020 IEEE International Conference on Robotics and Automation (ICRA)},
title={Beyond Photometric Consistency: Gradient-based Dissimilarity for Improving Visual Odometry and Stereo Matching},
videourl={https://www.youtube.com/watch?v=cqv7k-BK0g0},
url={https://arxiv.org/pdf/2004.04090.pdf},
year={2020},
pages={272-278},
doi={10.1109/ICRA40945.2020.9197483},
}

A. Barreto, S. Paulus, M. Varrelmann, and A. Mahlein, "Hyperspectral imaging of symptoms induced by Rhizoctonia solani in sugar beet: comparison of input data and different machine learning algorithms," Journal of Plant Diseases and Protection, vol. 127, iss. 4, 2020. doi:10.1007/s41348-020-00344-8
[BibTeX]

The fungal pathogen Rhizoctonia solani is one of the most important soil-borne diseases in sugar beet production worldwide. Root and crown rot caused by this fungus are traditionally recognized later in the cropping season by rating the above-ground symptoms like wilting and chlorosis on foliage, and dark brown lesions at the base of petioles. The present study was designed to evaluate noninvasive sensors and machine learning for measuring disease incidence and early detection. Eight-weeks-old plants were inoculated with the pathogen in two different concentrations and under controlled conditions. Hyperspectral images in the visible and near-infrared range from leaf were obtained in time-series. One hundred thirty and fifteen spectral features were selected in two levels by using the recursive feature elimination method (RFE) and a clustering approach. Subsequently, five types of machine-learning methods were employed to train four types of spectral data containing reflectance values, vegetation indices, selected variables of the RFE process and selected variables of an RFE-clustering process. The best classifier was obtained from a partial least squares modeling process and required a number of 15 spectral features, which include first and second derivatives of the wavelength spectrum as well as the Ctr3, EVI and PSSRa vegetation index. This investigation proves that under controlled conditions early detection of indirect symptoms caused by Rhizoctonia root rot in sugar-beet plants is possible. Scoring of disease incidence of Rhizoctonia root rot at 10 dai was 3 to 5 times higher with a machine-learning classifier in comparison with the human visual rating.

@Article{barreto2020,
author = {Barreto, Abel and Paulus, Stefan and Varrelmann, Mark and Mahlein, Anne-Katrin},
title = {Hyperspectral imaging of symptoms induced by Rhizoctonia solani in sugar beet: comparison of input data and different machine learning algorithms},
journal = {Journal of Plant Diseases and Protection},
volume = {127},
year = {2020},
number = {4},
issn = {1861-3837},
abstract = {The fungal pathogen Rhizoctonia solani is one of the most important soil-borne diseases in sugar beet production worldwide. Root and crown rot caused by this fungus are traditionally recognized later in the cropping season by rating the above-ground symptoms like wilting and chlorosis on foliage, and dark brown lesions at the base of petioles. The present study was designed to evaluate noninvasive sensors and machine learning for measuring disease incidence and early detection. Eight-weeks-old plants were inoculated with the pathogen in two different concentrations and under controlled conditions. Hyperspectral images in the visible and near-infrared range from leaf were obtained in time-series. One hundred thirty and fifteen spectral features were selected in two levels by using the recursive feature elimination method (RFE) and a clustering approach. Subsequently, five types of machine-learning methods were employed to train four types of spectral data containing reflectance values, vegetation indices, selected variables of the RFE process and selected variables of an RFE-clustering process. The best classifier was obtained from a partial least squares modeling process and required a number of 15 spectral features, which include first and second derivatives of the wavelength spectrum as well as the Ctr3, EVI and PSSRa vegetation index. This investigation proves that under controlled conditions early detection of indirect symptoms caused by Rhizoctonia root rot in sugar-beet plants is possible. Scoring of disease incidence of Rhizoctonia root rot at 10 dai was 3 to 5 times higher with a machine-learning classifier in comparison with the human visual rating.},
doi = {10.1007/s41348-020-00344-8},
}

F. Savian, M. Martini, P. Ermacora, S. Paulus, and A. Mahlein, "Prediction of the Kiwifruit Decline Syndrome in Diseased Orchards by Remote Sensing," Remote Sensing, vol. 12, iss. 14, 2020. doi:10.3390/rs12142194
[BibTeX] [PDF]

Eight years after the first record in Italy, Kiwifruit Decline (KD), a destructive disease causing root rot, has already affected more than 25% of the area under kiwifruit cultivation in Italy. Diseased plants are characterised by severe decay of the fine roots and sudden wilting of the canopy, which is only visible after the season’s first period of heat (July–August). The swiftness of symptom appearance prevents correct timing and positioning for sampling of the disease, and is therefore a barrier to aetiological studies. The aim of this study is to test the feasibility of thermal and multispectral imaging for the detection of KD using an unsupervised classifier. Thus, RGB, multispectral and thermal data from a kiwifruit orchard, with healthy and diseased plants, were acquired simultaneously during two consecutive growing seasons (2017–2018) using an Unmanned Aerial Vehicle (UAV) platform. Data reduction was applied to the clipped areas of the multispectral and thermal data from the 2017 survey. Reduced data were then classified with two unsupervised algorithms, a K-means and a hierarchical method. The plant vigour (canopy size and presence/absence of wilted leaves) and the health shifts exhibited by asymptomatic plants between 2017 and 2018 were evaluated from RGB data via expert assessment and used as the ground truth for cluster interpretation. Multispectral data showed a high correlation with plant vigour, while temperature data demonstrated a good potential use in predicting health shifts, especially in highly vigorous plants that were asymptomatic in 2017 and became symptomatic in 2018. The accuracy of plant vigour assessment was above 73% when using multispectral data, while clustering of the temperature data allowed the prediction of disease outbreak one year in advance, with an accuracy of 71%. Based on our results, the unsupervised clustering of remote sensing data could be a reliable tool for the identification of sampling areas, and can greatly improve aetiological studies of this new disease in kiwifruit.

@Article{rs12142194,
author = {Savian, Francesco and Martini, Marta and Ermacora, Paolo and Paulus, Stefan and Mahlein, Anne-Katrin},
title = {Prediction of the Kiwifruit Decline Syndrome in Diseased Orchards by Remote Sensing},
journal = {Remote Sensing},
volume = {12},
year = {2020},
number = {14},
article-number= {2194},
url = {https://www.mdpi.com/2072-4292/12/14/2194},
issn = {2072-4292},
abstract = {Eight years after the first record in Italy, Kiwifruit Decline (KD), a destructive disease causing root rot, has already affected more than 25% of the area under kiwifruit cultivation in Italy. Diseased plants are characterised by severe decay of the fine roots and sudden wilting of the canopy, which is only visible after the season’s first period of heat (July–August). The swiftness of symptom appearance prevents correct timing and positioning for sampling of the disease, and is therefore a barrier to aetiological studies. The aim of this study is to test the feasibility of thermal and multispectral imaging for the detection of KD using an unsupervised classifier. Thus, RGB, multispectral and thermal data from a kiwifruit orchard, with healthy and diseased plants, were acquired simultaneously during two consecutive growing seasons (2017–2018) using an Unmanned Aerial Vehicle (UAV) platform. Data reduction was applied to the clipped areas of the multispectral and thermal data from the 2017 survey. Reduced data were then classified with two unsupervised algorithms, a K-means and a hierarchical method. The plant vigour (canopy size and presence/absence of wilted leaves) and the health shifts exhibited by asymptomatic plants between 2017 and 2018 were evaluated from RGB data via expert assessment and used as the ground truth for cluster interpretation. Multispectral data showed a high correlation with plant vigour, while temperature data demonstrated a good potential use in predicting health shifts, especially in highly vigorous plants that were asymptomatic in 2017 and became symptomatic in 2018. The accuracy of plant vigour assessment was above 73% when using multispectral data, while clustering of the temperature data allowed the prediction of disease outbreak one year in advance, with an accuracy of 71%. Based on our results, the unsupervised clustering of remote sensing data could be a reliable tool for the identification of sampling areas, and can greatly improve aetiological studies of this new disease in kiwifruit.},
doi = {10.3390/rs12142194},
}

F. Jonard, D. S. Cannière, N. Brüggemann, P. Gentine, S. D. J. Gianotti, G. Lobet, D. G. Miralles, C. Montzka, B. R. Pagán, U. Rascher, and H. Vereecken, "Value of sun-induced chlorophyll fluorescence for quantifying hydrological states and fluxes: Current status and challenges," Agricultural and Forest Meteorology, vol. 291, p. 108088, 2020. doi:10.1016/j.agrformet.2020.108088
[BibTeX]

Predictions of hydrological states and fluxes, especially transpiration, are poorly constrained in hydrological models due to large uncertainties in parameterization and process description. Novel technologies like remote sensing of sun-induced chlorophyll fluorescence (SIF)—which provides information from the photosynthetic apparatus—may help in constraining water cycle components. This paper discusses the nature of the plant physiological basis of the fluorescence signal and analyses the current literature linking hydrological states and fluxes to SIF. Given the connection between photosynthesis and transpiration, through the water use efficiency, SIF may serve as a pertinent constraint for hydrological models. The FLuorescence EXplorer (FLEX) satellite, planned to be launched in 2023, is expected to provide spatially high-resolution measurements of red and far-red SIF complementing the products from existing satellite missions and the high-temporal resolution products from upcoming geostationary missions. This new data stream may allow us to better constrain plant transpiration, assess the impacts of water stress on plants, and infer processes occurring in the root zone through the soil-plant water column. To make optimal use of this data, progress needs to be made in 1) our process representation of spatially aggregated fluorescence signals from spaceborne SIF instruments, 2) integration of fluorescence processes in hydrological models—particularly when paired with other satellite data, 3) quantifying the impact of soil moisture on SIF across scales, and 4) assessment of the accuracy of SIF measurements—especially from space.

@Article{jonard2020108088,
title = "Value of sun-induced chlorophyll fluorescence for quantifying hydrological states and fluxes: Current status and challenges",
journal = "Agricultural and Forest Meteorology",
volume = "291",
pages = "108088",
year = "2020",
issn = "0168-1923",
doi = "10.1016/j.agrformet.2020.108088",
author = "F. Jonard and S. De Cannière and N. Brüggemann and P. Gentine and D.J. Short Gianotti and G. Lobet and D.G. Miralles and C. Montzka and B.R. Pagán and U. Rascher and H. Vereecken",
keywords = "Solar-induced chlorophyll fluorescence, Soil water availability, Drought stress, Transpiration, Hydrological processes, Radiative transfer model",
abstract = "Predictions of hydrological states and fluxes, especially transpiration, are poorly constrained in hydrological models due to large uncertainties in parameterization and process description. Novel technologies like remote sensing of sun-induced chlorophyll fluorescence (SIF)—which provides information from the photosynthetic apparatus—may help in constraining water cycle components. This paper discusses the nature of the plant physiological basis of the fluorescence signal and analyses the current literature linking hydrological states and fluxes to SIF. Given the connection between photosynthesis and transpiration, through the water use efficiency, SIF may serve as a pertinent constraint for hydrological models. The FLuorescence EXplorer (FLEX) satellite, planned to be launched in 2023, is expected to provide spatially high-resolution measurements of red and far-red SIF complementing the products from existing satellite missions and the high-temporal resolution products from upcoming geostationary missions. This new data stream may allow us to better constrain plant transpiration, assess the impacts of water stress on plants, and infer processes occurring in the root zone through the soil-plant water column. To make optimal use of this data, progress needs to be made in 1) our process representation of spatially aggregated fluorescence signals from spaceborne SIF instruments, 2) integration of fluorescence processes in hydrological models—particularly when paired with other satellite data, 3) quantifying the impact of soil moisture on SIF across scales, and 4) assessment of the accuracy of SIF measurements—especially from space.",
}

P. Gaugler, V. Gaugler, M. Kamleitner, and G. Schaaf, "Extraction and Quantification of Soluble, Radiolabeled Inositol Polyphosphates from Different Plant Species using SAX-HPLC," Journal of Visualized Experiments, iss. 160, 2020. doi:10.3791/61495
[BibTeX] [PDF] [Video]

@Article{gaugler_gaugler_kamleitner_schaaf_2020,
title = {Extraction and Quantification of Soluble, Radiolabeled Inositol Polyphosphates from Different Plant Species using SAX-HPLC},
videourl  = {https://www.youtube.com/watch?v=X1lQTTSzNsc},
url = {https://www.jove.com/video/61495/extraction-quantification-soluble-radiolabeled-inositol},
doi = {10.3791/61495},
number = {160},
journal = {Journal of Visualized Experiments},
author = {Philipp Gaugler and Verena Gaugler and Marília Kamleitner and Gabriel Schaaf},
year = {2020},
month = {Jun},
}

C. H. Bock, J. G. A. Barbedo, E. D. M. Ponte, D. Bohnenkamp, and A. Mahlein, "From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy," Phytopathology Research 2, 2020. doi:10.1186/s42483-020-00049-8
[BibTeX] [PDF]

The severity of plant diseases, traditionally the proportion of the plant tissue exhibiting symptoms, is a key quantitative variable to know for many diseases and is prone to error. Good quality disease severity data should be accurate (close to the true value). Earliest quantification of disease severity was by visual estimates. Sensor-based image analysis including visible spectrum and hyperspectral and multispectral sensors are established technologies that promise to substitute, or complement visual ratings. Indeed, these technologies have measured disease severity accurately under controlled conditions but are yet to demonstrate their full potential for accurate measurement under field conditions. Sensor technology is advancing rapidly, and artificial intelligence may help overcome issues for automating severity measurement under hyper-variable field conditions. The adoption of appropriate scales, training, instruction and aids (standard area diagrams) has contributed to improved accuracy of visual estimates. The apogee of accuracy for visual estimation is likely being approached, and any remaining increases in accuracy are likely to be small. Due to automation and rapidity, sensor-based measurement offers potential advantages compared with visual estimates, but the latter will remain important for years to come. Mobile, automated sensor-based systems will become increasingly common in controlled conditions and, eventually, in the field for measuring plant disease severity for the purpose of research and decision making.

@Article{bock2020,
title = "From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy",
journal = "Phytopathology Research 2",
year = "2020",
issn = "2524-4167",
doi = "10.1186/s42483-020-00049-8",
url = "https://phytopatholres.biomedcentral.com/articles/10.1186/s42483-020-00049-8",
author = "Clive H. Bock and Jayme G. A. Barbedo and Emerson M. Del Ponte and David Bohnenkamp and Anne-Katrin Mahlein",
abstract = "The severity of plant diseases, traditionally the proportion of the plant tissue exhibiting symptoms, is a key quantitative variable to know for many diseases and is prone to error. Good quality disease severity data should be accurate (close to the true value). Earliest quantification of disease severity was by visual estimates. Sensor-based image analysis including visible spectrum and hyperspectral and multispectral sensors are established technologies that promise to substitute, or complement visual ratings. Indeed, these technologies have measured disease severity accurately under controlled conditions but are yet to demonstrate their full potential for accurate measurement under field conditions. Sensor technology is advancing rapidly, and artificial intelligence may help overcome issues for automating severity measurement under hyper-variable field conditions. The adoption of appropriate scales, training, instruction and aids (standard area diagrams) has contributed to improved accuracy of visual estimates. The apogee of accuracy for visual estimation is likely being approached, and any remaining increases in accuracy are likely to be small. Due to automation and rapidity, sensor-based measurement offers potential advantages compared with visual estimates, but the latter will remain important for years to come. Mobile, automated sensor-based systems will become increasingly common in controlled conditions and, eventually, in the field for measuring plant disease severity for the purpose of research and decision making.",
}

B. Müller, F. Hoffmann, T. Heckelei, C. Müller, T. W. Hertel, G. J. Polhill, M. [. Wijk], T. Achterbosch, P. Alexander, C. Brown, D. Kreuer, F. Ewert, J. Ge, J. D. A. Millington, R. Seppelt, P. H. Verburg, and H. Webber, "Modelling food security: Bridging the gap between the micro and the macro scale," Global Environmental Change, vol. 63, p. 102085, 2020. doi:10.1016/j.gloenvcha.2020.102085
[BibTeX]

Achieving food and nutrition security for all in a changing and globalized world remains a critical challenge of utmost importance. The development of solutions benefits from insights derived from modelling and simulating the complex interactions of the agri-food system, which range from global to household scales and transcend disciplinary boundaries. A wide range of models based on various methodologies (from food trade equilibrium to agent-based) seek to integrate direct and indirect drivers of change in land use, environment and socio-economic conditions at different scales. However, modelling such interaction poses fundamental challenges, especially for representing non-linear dynamics and adaptive behaviours. We identify key pieces of the fragmented landscape of food security modelling, and organize achievements and gaps into different contextual domains of food security (production, trade, and consumption) at different spatial scales. Building on in-depth reflection on three core issues of food security – volatility, technology, and transformation – we identify methodological challenges and promising strategies for advancement. We emphasize particular requirements related to the multifaceted and multiscale nature of food security. They include the explicit representation of transient dynamics to allow for path dependency and irreversible consequences, and of household heterogeneity to incorporate inequality issues. To illustrate ways forward we provide good practice examples using meta-modelling techniques, non-equilibrium approaches and behavioural-based modelling endeavours. We argue that further integration of different model types is required to better account for both multi-level agency and cross-scale feedbacks within the food system.

@Article{muller2020102085,
title = "Modelling food security: Bridging the gap between the micro and the macro scale",
journal = "Global Environmental Change",
volume = "63",
pages = "102085",
year = "2020",
issn = "0959-3780",
doi = "10.1016/j.gloenvcha.2020.102085",
author = "Birgit Müller and Falk Hoffmann and Thomas Heckelei and Christoph Müller and Thomas W. Hertel and J. Gareth Polhill and Mark [van Wijk] and Thom Achterbosch and Peter Alexander and Calum Brown and David Kreuer and Frank Ewert and Jiaqi Ge and James D.A. Millington and Ralf Seppelt and Peter H. Verburg and Heidi Webber",
keywords = "Food security, Multi-scale interactions, Model integration, Agent-based models, Economic equilibrium models, Crop models, Social-ecological feedbacks, Land use",
abstract = "Achieving food and nutrition security for all in a changing and globalized world remains a critical challenge of utmost importance. The development of solutions benefits from insights derived from modelling and simulating the complex interactions of the agri-food system, which range from global to household scales and transcend disciplinary boundaries. A wide range of models based on various methodologies (from food trade equilibrium to agent-based) seek to integrate direct and indirect drivers of change in land use, environment and socio-economic conditions at different scales. However, modelling such interaction poses fundamental challenges, especially for representing non-linear dynamics and adaptive behaviours. We identify key pieces of the fragmented landscape of food security modelling, and organize achievements and gaps into different contextual domains of food security (production, trade, and consumption) at different spatial scales. Building on in-depth reflection on three core issues of food security – volatility, technology, and transformation – we identify methodological challenges and promising strategies for advancement. We emphasize particular requirements related to the multifaceted and multiscale nature of food security. They include the explicit representation of transient dynamics to allow for path dependency and irreversible consequences, and of household heterogeneity to incorporate inequality issues. To illustrate ways forward we provide good practice examples using meta-modelling techniques, non-equilibrium approaches and behavioural-based modelling endeavours. We argue that further integration of different model types is required to better account for both multi-level agency and cross-scale feedbacks within the food system.",
}

L. Zabawa, A. Kicherer, L. Klingbeil, R. Töpfer, H. Kuhlmann, and R. Roscher, "Counting of grapevine berries in images via semantic segmentation using convolutional neural networks," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 164, pp. 73-83, 2020. doi:10.1016/j.isprsjprs.2020.04.002
[BibTeX] [PDF] [Video]

The extraction of phenotypic traits is often very time and labour intensive. Especially the investigation in viticulture is restricted to an on-site analysis due to the perennial nature of grapevine. Traditionally skilled experts examine small samples and extrapolate the results to a whole plot. Thereby different grapevine varieties and training systems, e.g. vertical shoot positioning (VSP) and semi minimal pruned hedges (SMPH) pose different challenges. In this paper we present an objective framework based on automatic image analysis which works on two different training systems. The images are collected semi automatic by a camera system which is installed in a modified grape harvester. The system produces overlapping images from the sides of the plants. Our framework uses a convolutional neural network to detect single berries in images by performing a semantic segmentation. Each berry is then counted with a connected component algorithm. We compare our results with the Mask-RCNN, a state-of-the-art network for instance segmentation and with a regression approach for counting. The experiments presented in this paper show that we are able to detect green berries in images despite of different training systems. We achieve an accuracy for the berry detection of 94.0% in the VSP and 85.6% in the SMPH.

@Article{zabawa202073,
title = "Counting of grapevine berries in images via semantic segmentation using convolutional neural networks",
journal = "ISPRS Journal of Photogrammetry and Remote Sensing",
volume = "164",
pages = "73 - 83",
year = "2020",
issn = "0924-2716",
videourl = "https://www.youtube.com/watch?v=4DugxfYMUsk",
doi = "10.1016/j.isprsjprs.2020.04.002",
url = "https://arxiv.org/pdf/2004.14010",
author = "Laura Zabawa and Anna Kicherer and Lasse Klingbeil and Reinhard Töpfer and Heiner Kuhlmann and Ribana Roscher",
keywords = "Deep learning, Semantic segmentation, Geoinformation, High-throughput analysis, Plant phenotyping, Vitis",
abstract = "The extraction of phenotypic traits is often very time and labour intensive. Especially the investigation in viticulture is restricted to an on-site analysis due to the perennial nature of grapevine. Traditionally skilled experts examine small samples and extrapolate the results to a whole plot. Thereby different grapevine varieties and training systems, e.g. vertical shoot positioning (VSP) and semi minimal pruned hedges (SMPH) pose different challenges. In this paper we present an objective framework based on automatic image analysis which works on two different training systems. The images are collected semi automatic by a camera system which is installed in a modified grape harvester. The system produces overlapping images from the sides of the plants. Our framework uses a convolutional neural network to detect single berries in images by performing a semantic segmentation. Each berry is then counted with a connected component algorithm. We compare our results with the Mask-RCNN, a state-of-the-art network for instance segmentation and with a regression approach for counting. The experiments presented in this paper show that we are able to detect green berries in images despite of different training systems. We achieve an accuracy for the berry detection of 94.0% in the VSP and 85.6% in the SMPH.",
}

A. Bonerath, J. -H. Haunert, and B. Niedermann, "Tight Rectilinear Hulls of Simple Polygons," in Proceedings of the 36th European Workshop on Computational Geometry (EuroCG'20) , 2020.
[BibTeX] [PDF] [Video]

A polygon is called C-oriented if the orientations of all its edges stem from a pre-defined set C. The schematization of a polygon is then a C-oriented polygon that describes and simplifies the shape of the input polygon with respect to given hard and soft constraints. We study the case that the C-oriented polygon needs to contain the input polygon such that it is tight in the sense that it cannot be shrunk without starting to overlap with the input polygon; we call this a tight C-hull of the polygon. We restrict the tight C-hull to be a simple polygon. We aim at a tight C-hull that optimally balances the number of bends, the total edge length and the enclosed area. For the case that both polygons are rectilinear, we present a dynamic-programming approach that yields such a tight hull in polynomial time. For arbitrary simple polygons we can use the same approach to obtain approximate tight rectilinear hulls.

@InProceedings{bhn-trhsp-20,
abstract = {A polygon is called C-oriented if the orientations of all its edges stem from a pre-defined set C. The schematization of a polygon is then a C-oriented polygon that describes and simplifies the shape of the input polygon with respect to given hard and soft constraints. We study the case that the C-oriented polygon needs to contain the input polygon such that it is tight in the sense that it cannot be shrunk without starting to overlap with the input polygon; we call this a tight C-hull of the polygon. We restrict the tight C-hull to be a simple polygon. We aim at a tight C-hull that optimally balances the number of bends, the total edge length and the enclosed area. For the case that both polygons are rectilinear, we present a dynamic-programming approach that yields such a tight hull in polynomial time. For arbitrary simple polygons we can use the same approach to obtain approximate tight rectilinear hulls.},
author = {Bonerath, A. and Haunert, J.-H. and Niedermann, B.},
booktitle = {Proceedings of the 36th European Workshop on Computational Geometry (EuroCG'20)},
note = {Preprint.},
title = {{Tight Rectilinear Hulls of Simple Polygons}},
videourl  = {https://www.youtube.com/watch?v=GZTS5fs0FG4},
url = {https://www1.pub.informatik.uni-wuerzburg.de/eurocg2020/data/uploads/papers/eurocg20_paper_83.pdf},
year = {2020},
}

E. Heinz, C. Holst, H. Kuhlmann, and L. Klingbeil, "Design and Evaluation of a Permanently Installed Plane-Based Calibration Field for Mobile Laser Scanning Systems," Remote Sensing, vol. 12, iss. 3, 2020. doi:10.3390/rs12030555
[BibTeX] [PDF]

Mobile laser scanning has become an established measuring technique that is used for many applications in the fields of mapping, inventory, and monitoring. Due to the increasing operationality of such systems, quality control w.r.t. calibration and evaluation of the systems becomes more and more important and is subject to on-going research. This paper contributes to this topic by using tools from geodetic configuration analysis in order to design and evaluate a plane-based calibration field for determining the lever arm and boresight angles of a 2D laser scanner w.r.t. a GNSS/IMU unit (Global Navigation Satellite System, Inertial Measurement Unit). In this regard, the impact of random, systematic, and gross observation errors on the calibration is analyzed leading to a plane setup that provides accurate and controlled calibration parameters. The designed plane setup is realized in the form of a permanently installed calibration field. The applicability of the calibration field is tested with a real mobile laser scanning system by frequently repeating the calibration. Empirical standard deviations of <1 ... 1.5 mm for the lever arm and <0.005 ∘ for the boresight angles are obtained, which was priorly defined to be the goal of the calibration. In order to independently evaluate the mobile laser scanning system after calibration, an evaluation environment is realized consisting of a network of control points as well as TLS (Terrestrial Laser Scanning) reference point clouds. Based on the control points, both the horizontal and vertical accuracy of the system is found to be < 10 mm (root mean square error). This is confirmed by comparisons to the TLS reference point clouds indicating a well calibrated system. Both the calibration field and the evaluation environment are permanently installed and can be used for arbitrary mobile laser scanning systems.

@Article{rs12030555,
author = {Heinz, Erik and Holst, Christoph and Kuhlmann, Heiner and Klingbeil, Lasse},
title = {Design and Evaluation of a Permanently Installed Plane-Based Calibration Field for Mobile Laser Scanning Systems},
journal = {Remote Sensing},
volume = {12},
year = {2020},
number = {3},
article-number= {555},
url = {https://www.mdpi.com/2072-4292/12/3/555},
issn = {2072-4292},
abstract = {Mobile laser scanning has become an established measuring technique that is used for many applications in the fields of mapping, inventory, and monitoring. Due to the increasing operationality of such systems, quality control w.r.t. calibration and evaluation of the systems becomes more and more important and is subject to on-going research. This paper contributes to this topic by using tools from geodetic configuration analysis in order to design and evaluate a plane-based calibration field for determining the lever arm and boresight angles of a 2D laser scanner w.r.t. a GNSS/IMU unit (Global Navigation Satellite System, Inertial Measurement Unit). In this regard, the impact of random, systematic, and gross observation errors on the calibration is analyzed leading to a plane setup that provides accurate and controlled calibration parameters. The designed plane setup is realized in the form of a permanently installed calibration field. The applicability of the calibration field is tested with a real mobile laser scanning system by frequently repeating the calibration. Empirical standard deviations of <1 ... 1.5 mm for the lever arm and <0.005 ∘ for the boresight angles are obtained, which was priorly defined to be the goal of the calibration. In order to independently evaluate the mobile laser scanning system after calibration, an evaluation environment is realized consisting of a network of control points as well as TLS (Terrestrial Laser Scanning) reference point clouds. Based on the control points, both the horizontal and vertical accuracy of the system is found to be < 10 mm (root mean square error). This is confirmed by comparisons to the TLS reference point clouds indicating a well calibrated system. Both the calibration field and the evaluation environment are permanently installed and can be used for arbitrary mobile laser scanning systems.},
doi = {10.3390/rs12030555},
}

2019

O. Vysotska, H. Kuhlmann, and C. Stachniss, "UAVs Towards Sustainable Crop Production," in Workshop at Robotics: Science and Systems , 2019.
[BibTeX] [PDF]

@InProceedings{vysotska2019rsswsabstract,
author = {O. Vysotska and H. Kuhlmann and C. Stachniss},
title = {{UAVs Towards Sustainable Crop Production}},
booktitle = {Workshop at Robotics: Science and Systems},
year = {2019},
note = {Abstract},
URL = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/vysotska2019rsswsabstract.pdf},
}

F. Seiffarth, T. Horvath, and S. Wrobel, "Maximal Closed Set and Half-Space Separations in Finite Closure Systems," in ECML/PKDD 2019 and has appeared in the Lecture Notes in Computer Science, Machine Learning and Knowledge Discovery in Databases - European Conference , 2019. doi:10.1007/978-3-030-46150-8_2
[BibTeX] [PDF]

@InProceedings{seiffarth2019maximal,
title = {Maximal Closed Set and Half-Space Separations in Finite Closure Systems},
author = {Florian Seiffarth and Tamas Horvath and Stefan Wrobel},
year = {2019},
url={https://arxiv.org/pdf/2001.04417.pdf},
doi={10.1007/978-3-030-46150-8_2},
booktitle = {ECML/PKDD 2019 and has appeared in the Lecture Notes in Computer Science, Machine Learning and Knowledge Discovery in Databases - European Conference},
}

J. Behley, M. Garbade, A. Milioto, J. Quenzel, S. Behnke, C. Stachniss, and J. Gall, "SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences." 2019. doi:10.1109/iccv.2019.00939
[BibTeX] [PDF] [Video]

@InProceedings{behley2019iccv,
author = {J. Behley and M. Garbade and A. Milioto and J. Quenzel and S. Behnke and C. Stachniss and J. Gall},
title = {{SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences}},
doi={10.1109/iccv.2019.00939},
booktitle = iccv,
year = {2019},
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/behley2019iccv.pdf},
videourl = {http://www.ipb.uni-bonn.de/html/projects/semantic_kitti/videos/teaser.mp4},
}

E. Palazzolo, J. Behley, P. Lottes, P. Giguère, and C. Stachniss, "ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residuals," in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , 2019. doi:10.1109/iros40897.2019.8967590
[BibTeX] [PDF] [Code] [Video]

@InProceedings{palazzolo2019iros,
author = {E. Palazzolo and J. Behley and P. Lottes and P. Gigu\`ere and C. Stachniss},
title = {{ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residuals}},
booktitle = {Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)},
doi={10.1109/iros40897.2019.8967590},
year = {2019},
url = {http://www.ipb.uni-bonn.de/pdfs/palazzolo2019iros.pdf},
codeurl = {https://github.com/PRBonn/refusion},
videourl = {https://youtu.be/1P9ZfIS5-p4},
}

X. Chen, A. Milioto, E. Palazzolo, P. Giguère, J. Behley, and C. Stachniss, "SuMa++: Efficient LiDAR-based Semantic SLAM," in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , 2019. doi:10.1109/iros40897.2019.8967704
[BibTeX] [PDF] [Code] [Video]

@InProceedings{chen2019iros,
author = {X. Chen and A. Milioto and E. Palazzolo and P. Giguère and J. Behley and C. Stachniss},
title = {{SuMa++: Efficient LiDAR-based Semantic SLAM}},
booktitle = {Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)},
doi={10.1109/iros40897.2019.8967704 },
year = {2019},
codeurl = {https://github.com/PRBonn/semantic_suma/},
videourl = {https://youtu.be/uo3ZuLuFAzk},
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/chen2019iros.pdf},
}

A. Milioto, I. Vizzo, J. Behley, and C. Stachniss, "RangeNet++: Fast and Accurate LiDAR Semantic Segmentation," in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , 2019. doi:10.1109/iros40897.2019.8967762
[BibTeX] [PDF] [Code] [Video]

@InProceedings{milioto2019iros,
author = {A. Milioto and I. Vizzo and J. Behley and C. Stachniss},
title = {{RangeNet++: Fast and Accurate LiDAR Semantic Segmentation}},
booktitle = {Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)},
year = {2019},
doi={10.1109/iros40897.2019.8967762},
codeurl = {https://github.com/PRBonn/lidar-bonnetal},
videourl = {https://youtu.be/wuokg7MFZyU},
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/milioto2019iros.pdf},
}

F. Yan, O. Vysotska, and C. Stachniss, " Global Localization on OpenStreetMap Using 4-bit Semantic Descriptors," in Proceedings of the European Conference on Mobile Robots (ECMR) , 2019. doi:10.1109/ecmr.2019.8870918
[BibTeX] [PDF]

@InProceedings{yan2019ecmr,
author = {F. Yan and O. Vysotska and C. Stachniss},
title = {{ Global Localization on OpenStreetMap Using 4-bit Semantic Descriptors}},
booktitle = {Proceedings of the European Conference on Mobile Robots (ECMR)},
year = {2019},
doi={10.1109/ecmr.2019.8870918},
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/yan2019ecmr.pdf},
}

R. Schirmer, P. Bieber, and C. Stachniss, "Coverage Path Planning in Belief Space ," in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2019. doi:10.1109/icra.2019.8793969
[BibTeX] [PDF]

@InProceedings{schirmer2019icra,
author = {R. Schirmer and P. Bieber and C. Stachniss},
title = {{Coverage Path Planning in Belief Space }},
booktitle = {Proc. of the IEEE Intl. Conf. on Robotics \& Automation (ICRA)},
year = {2019},
doi={10.1109/icra.2019.8793969},
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/schirmer2019icra.pdf},
}

K. Huang and C. Stachniss, "Accurate Direct Visual-Laser Odometry with Explicit Occlusion Handling and Plane Detection," in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2019. doi:10.1109/icra.2019.8793629
[BibTeX] [PDF]

@InProceedings{huang2019icra,
author = {K. Huang and C. Stachniss},
title = {{Accurate Direct Visual-Laser Odometry with Explicit Occlusion Handling and Plane Detection}},
url={https://www.researchgate.net/profile/Cyrill_Stachniss/publication/335144116_Accurate_Direct_Visual-Laser_Odometry_with_Explicit_Occlusion_Handling_and_Plane_Detection/links/5f60a58a299bf1d43c05021b/Accurate-Direct-Visual-Laser-Odometry-with-Explicit-Occlusion-Handling-and-Plane-Detection.pdf},
booktitle = {Proc. of the IEEE Intl. Conf. on Robotics \& Automation (ICRA)},
year = {2019},
doi={10.1109/icra.2019.8793629},
}

A. Bonerath, B. Niedermann, and J. -H. Haunert, "Retrieving α-Shapes and Schematic Polygonal Approximations for Sets of Points within Queried Temporal Ranges," in Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems , New York, NY, USA, 2019, p. 249–258. doi:10.1145/3347146.3359087
[BibTeX] [PDF] [Video]

The interactive exploration of data requires data structures that can be repeatedly queried to obtain simple visualizations of parts of the data. We consider the scenario that the data is a set of points each associated with a time stamp and that the result of each query is visualized by an α-shape, which generalizes the concept of convex hulls. Instead of computing each shape independently, we suggest and analyze a simple data structure that aggregates the α-shapes of all possible queries. Once the data structure is built, it particularly allows us to query single α-shapes without retrieving the actual (possibly large) point set and thus to rapidly produce small previews of the queried data. We discuss the data structure for the original α-shapes as well as for a schematized version of α-shapes, which further simplifies the visualization. We evaluate the data structure on real-world data. The experiments indicate linear memory consumption with respect to the number of points, which makes the data structure applicable in practice, although the size is quadratic for a theoretic worst case example.

@InProceedings{bhn-rasspa-19,
author = {Bonerath, A. and Niedermann, B. and Haunert, J.-H.},
title = {{Retrieving α-Shapes and Schematic Polygonal Approximations for Sets of Points within Queried Temporal Ranges}},
abstract = {The interactive exploration of data requires data structures that can be repeatedly queried to obtain simple visualizations of parts of the data. We consider the scenario that the data is a set of points each associated with a time stamp and that the result of each query is visualized by an α-shape, which generalizes the concept of convex hulls. Instead of computing each shape independently, we suggest and analyze a simple data structure that aggregates the α-shapes of all possible queries. Once the data structure is built, it particularly allows us to query single α-shapes without retrieving the actual (possibly large) point set and thus to rapidly produce small previews of the queried data. We discuss the data structure for the original α-shapes as well as for a schematized version of α-shapes, which further simplifies the visualization. We evaluate the data structure on real-world data. The experiments indicate linear memory consumption with respect to the number of points, which makes the data structure applicable in practice, although the size is quadratic for a theoretic worst case example.},
booktitle = {Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems},
series = {SIGSPATIAL '19},
year = {2019},
isbn = {978-1-4503-6909-1},
location = {Chicago, IL, USA},
pages = {249--258},
numpages = {10},
videourl  = {https://www.youtube.com/watch?v=mlnUDhbMSfQ},
url = {http://doi.acm.org/10.1145/3347146.3359087},
doi = {10.1145/3347146.3359087},
acmid = {3359087},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {alpha-shape, data structure, point set, temporal range queries},
}

D. Laha, N. Parvin, A. Hofer, R. F. H. Giehl, N. Fernandez-Rebollo, N. von Wirén, A. Saiardi, H. J. Jessen, and G. Schaaf, "Arabidopsis ITPK1 and ITPK2 Have an Evolutionarily Conserved Phytic Acid Kinase Activity," ACS Chemical Biology, vol. 14, iss. 10, pp. 2127-2133, 2019. doi:10.1021/acschembio.9b00423
[BibTeX] [PDF]

@Article{doi:10.1021/acschembio.9b00423,
author = {Laha, Debabrata and Parvin, Nargis and Hofer, Alexandre and Giehl, Ricardo F. H. and Fernandez-Rebollo, Nicolas and von Wirén, Nicolaus and Saiardi, Adolfo and Jessen, Henning J. and Schaaf, Gabriel},
title = {Arabidopsis ITPK1 and ITPK2 Have an Evolutionarily Conserved Phytic Acid Kinase Activity},
journal = {ACS Chemical Biology},
volume = {14},
number = {10},
pages = {2127-2133},
year = {2019},
doi = {10.1021/acschembio.9b00423},
note = {PMID: 31525024},
url = {https://doi.org/10.1021/acschembio.9b00423},
eprint = {https://doi.org/10.1021/acschembio.9b00423},
}

H. Storm, K. Baylis, and T. Heckelei, "Machine learning in agricultural and applied economics," European Review of Agricultural Economics, 2019. doi:10.1093/erae/jbz033
[BibTeX] [PDF]

{This review presents machine learning (ML) approaches from an applied economist’s perspective. We first introduce the key ML methods drawing connections to econometric practice. We then identify current limitations of the econometric and simulation model toolbox in applied economics and explore potential solutions afforded by ML. We dive into cases such as inflexible functional forms, unstructured data sources and large numbers of explanatory variables in both prediction and causal analysis, and highlight the challenges of complex simulation models. Finally, we argue that economists have a vital role in addressing the shortcomings of ML when used for quantitative economic analysis.}

@Article{10.1093/erae/jbz033,
author = {Storm, Hugo and Baylis, Kathy and Heckelei, Thomas},
title = "{Machine learning in agricultural and applied economics}",
journal = {European Review of Agricultural Economics},
year = {2019},
month = {08},
abstract = "{This review presents machine learning (ML) approaches from an applied economist’s perspective. We first introduce the key ML methods drawing connections to econometric practice. We then identify current limitations of the econometric and simulation model toolbox in applied economics and explore potential solutions afforded by ML. We dive into cases such as inflexible functional forms, unstructured data sources and large numbers of explanatory variables in both prediction and causal analysis, and highlight the challenges of complex simulation models. Finally, we argue that economists have a vital role in addressing the shortcomings of ML when used for quantitative economic analysis.}",
issn = {0165-1587},
doi = {10.1093/erae/jbz033},
url = {https://doi.org/10.1093/erae/jbz033},
note = {jbz033},
eprint = {http://oup.prod.sis.lan/erae/advance-article-pdf/doi/10.1093/erae/jbz033/29194185/jbz033.pdf},
}

A. Mahlein, M. T. Kuska, S. Thomas, M. Wahabzada, J. Behmann, U. Rascher, and K. Kersting, "Quantitative and qualitative phenotyping of disease resistance of crops by hyperspectral sensors: seamless interlocking of phytopathology, sensors, and machine learning is needed!," Current Opinion in Plant Biology, vol. 50, 2019. doi:10.1016/j.pbi.2019.06.007
[BibTeX] [PDF]

@Article{mahlein2020,
author = {Mahlein, Anne-Katrin and Kuska, Matheus Thomas and Thomas, Stefan and Wahabzada, Mirwaes and Behmann, Jan and Rascher, Uwe and Kersting, Kristian},
title = "{Quantitative and qualitative phenotyping of disease resistance of crops by hyperspectral sensors: seamless interlocking of phytopathology, sensors, and machine learning is needed!}",
journal = {Current Opinion in Plant Biology},
volume = {50},
year = {2019},
month = {08},
doi = {10.1016/j.pbi.2019.06.007},
url = {https://www.phenorob.de/wp-content/uploads/2024/08/Mahlein_2019_Current-Opinion_50_156.pdf},
}

O. Vysotska and C. Stachniss, "Effective Visual Place Recognition Using Multi-Sequence Maps," IEEE Robotics and Automation Letters (RA-L), 2019. doi:10.1109/lra.2019.2897160
[BibTeX] [PDF] [Video]

@Article{vysotska2019ral,
author = {O. Vysotska and C. Stachniss},
title = {{Effective Visual Place Recognition Using Multi-Sequence Maps}},
journal = {IEEE Robotics and Automation Letters (RA-L)},
year = {2019},
doi={10.1109/lra.2019.2897160},
url = {http://www.ipb.uni-bonn.de/pdfs/vysotska2019ral.pdf},
videourl = {https://youtu.be/wFU0JoXTH3c},
}

N. Chebrolu, P. Lottes, T. Läbe, and C. Stachniss, "Robot Localization Based on Aerial Images for Precision Agriculture Tasks in Crop Fields," in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2019. doi:10.1109/icra.2019.8794030
[BibTeX] [PDF] [Video]

@InProceedings{chebrolu2019icra,
author = {N. Chebrolu and P. Lottes and T. Läbe and C. Stachniss},
title = {{Robot Localization Based on Aerial Images for Precision Agriculture Tasks in Crop Fields}},
doi={10.1109/icra.2019.8794030},
booktitle = {Proc. of the IEEE Intl. Conf. on Robotics \& Automation (ICRA)},
year = 2019,
url = {http://www.ipb.uni-bonn.de/pdfs/chebrolu2019icra.pdf},
videourl = {https://youtu.be/TlijLgoRLbc},
}

A. Milioto and C. Stachniss, "Bonnet: An Open-Source Training and Deployment Framework for Semantic Segmentation in Robotics using CNNs," in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2019. doi:10.1109/icra.2019.8793510
[BibTeX] [PDF] [Code] [Video]

@InProceedings{milioto2019icra,
author = {A. Milioto and C. Stachniss},
title = {{Bonnet: An Open-Source Training and Deployment Framework for Semantic Segmentation in Robotics using CNNs}},
booktitle = {Proc. of the IEEE Intl. Conf. on Robotics \& Automation (ICRA)},
year = {2019},
doi={10.1109/icra.2019.8793510},
url = {https://arxiv.org/abs/1802.08960},
codeurl = {https://github.com/Photogrammetry-Robotics-Bonn/bonnet},
videourl = {https://www.youtube.com/watch?v=tfeFHCq6YJs},
}

A. Milioto, L. Mandtler, and C. Stachniss, "Fast Instance and Semantic Segmentation Exploiting Local Connectivity, Metric Learning, and One-Shot Detection for Robotics," in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2019. doi:10.1109/icra.2019.8793593
[BibTeX] [PDF]

@InProceedings{milioto2019icra-fiass,
author = {A. Milioto and L. Mandtler and C. Stachniss},
title = {{Fast Instance and Semantic Segmentation Exploiting Local Connectivity, Metric Learning, and One-Shot Detection for Robotics}},
url={https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/milioto2019icra-fiass.pdf},
booktitle = {Proc. of the IEEE Intl. Conf. on Robotics \& Automation (ICRA)},
year = {2019},
doi={10.1109/icra.2019.8793593},
}

L. Nardi and C. Stachniss, "Uncertainty-Aware Path Planning for Navigation on Road Networks Using Augmented MDPs," in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2019. doi:10.1109/icra.2019.8794121
[BibTeX] [PDF] [Video]

@InProceedings{nardi2019icra-uapp,
author = {L. Nardi and C. Stachniss},
title = {{Uncertainty-Aware Path Planning for Navigation on Road Networks Using Augmented MDPs}},
doi={10.1109/icra.2019.8794121},
booktitle = {Proc. of the IEEE Intl. Conf. on Robotics \& Automation (ICRA)},
year = {2019},
url = {http://www.ipb.uni-bonn.de/pdfs/nardi2019icra-uapp.pdf},
videourl = {https://youtu.be/3PMSamgYzi4},
}

L. Nardi and C. Stachniss, "Actively Improving Robot Navigation On Different Terrains Using Gaussian Process Mixture Models," in Proc. of the IEEE Intl. Conf. on Robotics and Automation (ICRA) , 2019. doi:10.1109/icra.2019.8794079
[BibTeX] [PDF] [Video]

@InProceedings{nardi2019icra-airn,
author = {L. Nardi and C. Stachniss},
title = {{Actively Improving Robot Navigation On Different Terrains Using Gaussian Process Mixture Models}},
booktitle = {Proc. of the IEEE Intl. Conf. on Robotics and Automation (ICRA)},
year = {2019},
doi={10.1109/icra.2019.8794079},
url = {http://www.ipb.uni-bonn.de/pdfs/nardi2019icra-airn.pdf},
videourl = {https://youtu.be/DlMbP3u1g2Y},
}