INRAE et l’Université Agricole de Nankin (NAU) ont créé un laboratoire international pour renforcer leur collaboration de longue date en phénotypage avancé et modélisation des plantes. Ce partenariat vise à développer des techniques d’imagerie innovantes, relier les données phénotypiques aux modèles 3D et aux modèles de culture, et tester ces outils dans des expérimentations multi-sites en France et en Chine. L’objectif est d’améliorer la compréhension des interactions génotype-environnement et de prédire la performance des cultures dans des conditions variées, tout en formant la prochaine génération de chercheurs.
Enjeux scientifiques
- Développer des techniques d’imagerie pour caractériser la structure des plantes et des couverts.
- Relier les données phénotypiques aux modèles 3D de plantes et aux modèles de culture pour prédire les effets des traits sur la performance et la résilience aux évènements météorologiques.
- Tester ces outils dans des expérimentations multi-sites pour valider leur robustesse.
- Former étudiants et jeunes chercheurs, et diffuser les méthodes développées.
Objectifs scientifiques (OS)
OS 1 – Améliorer les techniques d’imagerie et des algorithmes 3D
Développer des méthodes pour estimer des traits clés (biomasse, azote, croissance des feuilles, nombre de talles, angle des feuilles, taux de remplissage des grains, etc.) à partir d’images RGB, LiDAR et multispectrales, en combinant observations au sol, drones et modèles 3D avec des algorithmes d’apprentissage automatique. Objectif : créer des jumeaux numériques des cultures et mieux comprendre les interactions génotype-environnement.
OS 2 – Intégrer les données phénotypiques dans les modèles de culture
Améliorer le modèle SiriusQuality pour inclure des paramètres spécifiques aux génotypes issus du phénotypage, afin de prédire la performance et la résilience des cultures face aux stress multiples (sécheresse, chaleur, déficit azoté). Tester ces améliorations sur des données issues d’expérimentations multi-sites.
OS 3 – Analyser la variabilité génétique des traits phénotypés
Étudier la variabilité génétique des traits observés en réponse aux stress (température, eau, azote) via des expérimentations multi-sites en Chine et en France. Utiliser ces données pour des analyses GWAS, prédictions génomiques et corrélations entre traits et rendement, et pour paramétrer les modèles de culture.
OS 4 – Former et diffuser
Organiser des échanges de chercheurs, des cours et des séminaires pour étudiants et scientifiques des deux institutions. Promouvoir les approches FAIR pour la gestion des données et renforcer la coopération internationale.
Membres
INRAE, UMR LEPSE: Dr. Pierre Martre (INRAE coordinateur), A. Besnier, Dr. Llorenç Cabrera-Bosquet, Dr. Christian Fournier, Dr. Boris Parent, Dr. François Tardieu, Dr. Randall Wisser
INRAE, UMR EMMAH: Samuel Buis, Dr. Sylvain Jay, Dr. Raul Lopez-Lozano, Dr. Marie Weiss
NAU: Prof. Yanfeng Ding (NAU coordinateur), Prof. Shouyang Liu (NAU executive coordinateur), Prof. Dong Jiang, Prof. Xiao Wang, Dr. Weiwei Li, Dr. Rui Yu, Dr. Chen Zhu
Publications
Cai D, Zhu C, de Solan B, MANCEAU L, Baret F, López-Lozano R, Buis, Martre P, Liu S (2025) Improving the prediction of phenotypic variability among genotypes through the assimilation of high-throughput phenotyping observations into crop growth model. Plant Phenomics, in press.
Cheng T, Lu N, Wang W, Zhang Q, Li D, YAO X, Tian Y, Zhu Y, Cao W, .Baret F, Liu F (2019) Estimation of nitrogen nutrition status in winter wheat from unmanned aerial vehicle based multi-angular multispectral imagery. Frontiers in Plant Science 10: 1601. https://doi.org/10.3389/fpls.2019.01601
David E, Madec S, Sadeghi-Tehran P, Aasen H, Zheng B, Liu S, Kirchgessner N, Ishikawa G, Nagasawa K, Badhon MA, Pozniak C, de Solan B, Hund A, Chapman SC, Baret F, Stavness I, Guo W (2020) 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. Plant Phenomics 2020: 3521852. https://doi.org/10.34133/2020/3521852
David E, Serouart M, Smith D, Madec S, Velumani K, Liu S, Wang X, Pinto F, Shafiee S, Tahir ISA, Tsujimoto H, Nasuda S, Zheng B, Kirchgessner N, Aasen H, Hund A, Sadhegi-Tehran P, Nagasawa K, Ishikawa G, Dandrifosse S, Carlier A, Dumont B, Mercatoris B, Evers B, Kuroki K, Wang H, Ishii M, Badhon MA, Pozniak C, LeBauer DS, Lillemo M, Poland J, Chapman S, de Solan B, Baret F, Stavness I, Guo W (2021) Global wheat head detection 2021: an improved dataset for benchmarking wheat head detection methods. Plant Phenomics 2021: 9846158. https://doi.org/10.34133/2021/9846158
Dong M, Liu S, Jiang R, Qi J, de Solan B, Comar A, Li L, Li W, Ding Y, Baret F (2024) Comparing and combining data-driven and model-driven approaches to monitor wheat green area index with high spatio-temporal resolution satellites. Remote Sensing of Environment 305: 114118. https://doi.org/10.1016/j.rse.2024.114118
Gao Y, Li L, Weiss M, Guo W, Shi M, Lu H, Jiang R, Ding Y, Nampally T, Rajalakshmi P, Baret F, Liu S (2024) Bridging real and simulated data for cross spatial resolution vegetation segmentation with application to rice crops. ISPRS Journal of Photogrammetry and Remote Sensing. 218: 133-150. https://doi.org/10.1016/j.isprsjprs.2024.10.007
Jiang J, Weiss M, Liu S, Baret F (2022) Effective GAI is best estimated from reflectance observations as compared to GAI and LAI: Demonstration for wheat and maize crops based on 3D radiative transfer simulations. Field Crops Research 283: 108538. https://doi.org/10.1016/j.fcr.2022.108538
Jiang J, Weiss M, Liu S, Rochdi N, Baret F (2020) Speeding up 3D radiative transfer simulations: A physically based metamodel of canopy reflectance dependency on wavelength, leaf biochemical composition and soil reflectance. Remote Sensing of Environment 237: 111614. https://doi.org/10.1016/j.rse.2019.111614
Jin X, Liu S, Baret F, Hemerlé M, Comar A (2017) Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. Remote Sensing of Environment 198: 105-114. https://doi.org/10.1016/j.rse.2017.06.007
Jin S, Su Y, Zhang Y, Song S, Li Q, Liu Z, Ma Q, Ge Y, Liu L, Ding Y, Baret F (2021) Exploring seasonal and circadian rhythms in structural traits of field maize from LiDAR time series. Plant Phenomics 2021: 9895241. https://doi.org/10.34133/2021/9895241
Jin S, Sun X, Wu F, Su Y, Li Y, Song S, Xu K, Ma Q, Baret F, Jiang D, Dring Y (2021) Lidar sheds new light on plant phenomics for plant breeding and management: Recent advances and future prospects. ISPRS Journal of Photogrammetry and Remote Sensing 171: 202-223. https://doi.org/10.1016/j.isprsjprs.2020.11.006
Li W, Li D, Liu S, Baret F, Ma Z, He C, Warner TA, Guo C, Cheng T, Zhu Y, Cao W, Yao X (2023) RSARE: A physically-based vegetation index for estimating wheat green LAI to mitigate the impact of leaf chlorophyll content and residue-soil background. ISPRS Journal of Photogrammetry and Remote Sensing 200: 138–152. https://doi.org/10.1016/j.isprsjprs.2023.05.012
Li L, Mu X, Qi J, Pisek J, Roosjen P, Yan G, Huang H, Liu S, Baret F (2021) Characterizing reflectance anisotropy of background soil in open-canopy plantations using UAV-based multiangular images. ISPRS Journal of Photogrammetry and Remote Sensing 177: 263-278. https://doi.org/10.1016/j.isprsjprs.2021.05.007
Li Y, Zhan X, Liu S, Lu H, Jiang R, Guo W, Chapman S, Ge Y, Solan B, Ding Y, Baret F (2023) Self-supervised plant phenotyping by combining domain adaptation with 3D plant model simulations: application to wheat leaf counting at seedling stage. Plant Phenomics 5: 0041. https://doi.org/10.34133/plantphenomics.0041
Liu S, Baret F, Abichou M, Boudon F, Thomas S, Zhao K, Fournier C, Andrieu B, Irfan K, Hemmerlé M, De Solan B (2017) Estimating wheat green area index from ground-based LiDAR measurement using a 3D canopy structure model. Agricultural and Forest Meteorology 247: 12-20. https://doi.org/10.1016/j.agrformet.2017.07.007
Liu S, Baret F, Abichou M, Manceau L, Andrieu B, Weiss M, Martre P (2021) Importance of the description of light interception in crop growth models. Plant Physiology 186: 977-997. https://doi.org/10.1093/plphys/kiab113
Liu S, Baret F, Allard D, Jin X, Andrieu B, Burger P, Hemmerlé M, Comar A (2017) A method to estimate plant density and plant spacing heterogeneity: application to wheat crops. Plant methods 13: 38. https://doi.org/10.1186/s13007-017-0187-1
Liu S, Baret F, Andrieu B, Abichou M, Allard D, De Solan B, Burger P (2017) Modeling the spatial distribution of plants on the row for wheat crops: Consequences on the green fraction at the canopy level. Computers and Electronics in Agriculture 136: 147-156. https://doi.org/10.1016/j.compag.2017.02.022
Liu S, Baret F, Andrieu B, Burger P, Hemmerlé M (2017) Estimation of wheat plant density at early stages using high resolution imagery. Frontiers in plant science 8: 739. https://doi.org/10.3389/fpls.2017.00739
Liu S, Jin S, Guo Q, Zhu Y, Baret F (2020) An algorithm for estimating field wheat canopy light interception based on Digital Plant Phenotyping Platform. Smart Agriculture 2: 87-98. https://doi.org/10.12133/j.smartag.2020.2.1.202002-SA004
Liu S, Martre P, Buis S, Abichou M, Andrieu B, Baret F (2019) Estimation of plant and canopy architectural traits using the Digital Plant Phenotyping Platform. Plant Physiology 181: 881-890. https://doi.org/10.1104/pp.19.00554
Madec S, Jin X, Lu H, De Solan B, Liu S, Duyme F, Heritier E, Baret F (2019) Ear density estimation from high resolution RGB imagery using deep learning technique. Agricultural and Forest Meteorology 264: 225-234. https://doi.org/10.1016/j.agrformet.2018.10.013
Manceau L, Albasha R, Liu S, Martre P (2020) SiriusQuality-BioMa-Irradiance-Component: A BioMA-SiriusQuality component of single and multi-layers big-leaf and sun/shade models of absorbed irradiance by crop canopies. Zenodo: 3820386 https://doi.org/https://doi.org/10.5281/zenodo.3820386
Wang J, Lopez-Lozano R, Weiss M, Buis S, Li W, Liu S, Baret F, Zhang J (2022) Crop specific inversion of PROSAIL to retrieve green area index (GAI) from several decametric satellites using a Bayesian framework. Remote Sensing of Environment 278: 113085. https://doi.org/10.1016/j.rse.2022.113085
Yang T, Jay S, Gao Y, Liu S, Baret F (2023) The balance between spectral and spatial information to estimate straw cereal plant density at early growth stages from optical sensors. Computers and Electronics in Agriculture 215: 108458. https://doi.org/10.1016/j.compag.2023.108458
Zhou J, Francois T, Tony P, John D, Daniel R, Neil H, Simon G, Cheng T, Zhu Y, Wang X, Jiang D, Ding Y (2018) Plant phenomics: history, present status and challenges. Journal of Nanjing Agricultural University 41: 580-588. https://nauxb.njau.edu.cn/#/digest?ArticleID=7898 (In Chinese, abstract English)
Zhu C, Liu S, Parent B, Yin X, de Solan, B, Jiang D, Ding Y, Baret F (2024) Genotype × environment × management analysis to define allometric rules between leaves and stems in wheat. Journal of Experimental Botany, erae291. https://doi.org/10.1093/jxb/erae291
