| Wilds: A benchmark of in-the-wild distribution shifts PW Koh, S Sagawa, H Marklund, SM Xie, M Zhang, A Balsubramani, ... International conference on machine learning, 5637-5664, 2021 | 1159 | 2021 |
| Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods E David, S Madec, P Sadeghi-Tehran, H Aasen, B Zheng, S Liu, ... Plant Phenomics 2020, 3521852, 2020 | 194 | 2020 |
| Extending the wilds benchmark for unsupervised adaptation S Sagawa, PW Koh, T Lee, I Gao, SM Xie, K Shen, A Kumar, W Hu, ... arXiv preprint arXiv:2112.05090, 2021 | 94 | 2021 |
| Global wheat head detection 2021: An improved dataset for benchmarking wheat head detection methods E David, M Serouart, D Smith, S Madec, K Velumani, S Liu, X Wang, ... Plant Phenomics, 2021 | 83* | 2021 |
| SegVeg: Segmenting RGB images into green and senescent vegetation by combining deep and shallow methods M Serouart, S Madec, E David, K Velumani, RL Lozano, M Weiss, F Baret Plant Phenomics, 2022 | 18 | 2022 |
| Plant detection and counting from high-resolution RGB images acquired from UAVs: comparison between deep-learning and handcrafted methods with application to maize, sugar beet … E David, G Daubige, F Joudelat, P Burger, A Comar, B de Solan, F Baret bioRxiv, 2021.04. 27.441631, 2021 | 13 | 2021 |
| VegAnn, Vegetation Annotation of multi-crop RGB images acquired under diverse conditions for segmentation S Madec, K Irfan, K Velumani, F Baret, E David, G Daubige, LB Samatan, ... Scientific Data 10 (1), 302, 2023 | 6 | 2023 |
| The P2S2 segmentation dataset: annotated in-field multi-crop RGB images acquired under various conditions S Madec, K Irfan, E David, K Velumani, G Daubige, J Labrosse, W Guo, ... 7th International Workshop on Image Analysis Methods in the Plant Sciences …, 2019 | 5 | 2019 |
| Global wheat head detection challenges: Winning models and application for head counting E David, F Ogidi, D Smith, S Chapman, B de Solan, W Guo, F Baret, ... Plant Phenomics 5, 0059, 2023 | 4* | 2023 |
| HERMES: Hybrid Error-corrector Model with inclusion of External Signals for nonstationary fashion time series E David, J Bellot, SL Corff arXiv preprint arXiv:2202.03224, 2022 | 3 | 2022 |
| Greenhouse Modules and Regenerative Life-Support Systems for Space L Poulet, D Schubert, C Zeidler, P Zabel, V Maiwald, E David, C Paillé AIAA SPACE 2013 Conference and Exposition, 5398, 2013 | 3 | 2013 |
| Identifiability of discrete input–output hidden Markov models with external signals E David, J Bellot, S Le Corff, L Lehéricy Statistics and Computing 34 (1), 54, 2024 | 2 | 2024 |
| Domain adaptation for plant organ detection with style transfer C James, Y Gu, S Chapman, W Guo, E David, S Madec, A Potgieter, ... 2021 Digital Image Computing: Techniques and Applications (DICTA), 1-9, 2021 | 2 | 2021 |
| Variational quantization for state space models E David, J Bellot, SL Corff arXiv preprint arXiv:2404.11117, 2024 | 1 | 2024 |
| From prototype to inference: A pipeline to apply deep learning in sorghum panicle detection C James, Y Gu, A Potgieter, E David, S Madec, W Guo, F Baret, ... Plant Phenomics 5, 0017, 2023 | 1 | 2023 |
| The challenge of robust trait estimates with Deep Learning on high resolution RGB images E David Université d'Avignon, 2021 | 1 | 2021 |
| Wilds: A benchmark of in-the-wild distribution shifts H Marklund, SM Xie, M Zhang, A Balsubramani, W Hu, M Yasunaga, ... arXiv preprint arXiv:2012.07421, 2020 | 1 | 2020 |
| A dictionary-based approach to reduce noise in fluorescent microscopy images L Nasser, P Coronado, E David, T Boudier 2017 IEEE 2nd International Conference on Signal and Image Processing (ICSIP …, 2017 | 1 | 2017 |
| Time series forecasting models applied on large datasets with inclusion of external signals E David Institut Polytechnique de Paris, 2024 | | 2024 |
| Le gorille, le ver de terre, la pieuvre, la blatte… et Miquel Barceló E David Esquisses-Revue d'esthétique en ligne, 2023 | | 2023 |
