INRAE and Nanjing Agricultural University (NAU) have established an international laboratory to strengthen their long-standing collaboration in advanced plant phenotyping and modeling. This partnership aims to develop innovative imaging techniques, link phenotypic data to 3D plant and crop models, and test these tools in multi-site experiments in France and China. The goal is to improve understanding of genotype–environment interactions and predict crop performance under diverse conditions, while training the next generation of researchers in plant phenotyping and modeling.
Scientific Challenges
- Develop imaging techniques to characterize plant and canopy architecture traits.
- Link phenomics data to 3D plant architecture and crop models to predict trait effects on performance.
- Validate these tools in multi-site experiments.
- Train students and young researchers and disseminate developed methods.
Scientific Objectives (SO)
SO 1 – Improve imaging techniques and 3D model algorithms
Develop methods to estimate key traits (biomass, nitrogen content, leaf growth, tiller number, leaf angle, grain filling rate, etc.) from RGB, LiDAR, and multispectral images, combining ground and UAV observations with 3D models and machine learning. Goal: create digital crop twins and better understand genotype–environment interactions.
SO 2 – Integrate phenomics data into crop models
Enhance the SiriusQuality model to include genotype-specific parameters from phenotyping, improving predictions of crop performance and resilience under multi-stress conditions. Validate improvements using multi-site datasets.
SO 3 – Analyse the genetic variability of phenotyped traits
Analyze genetic variability of traits under heat, drought, and nitrogen stress through multi-site experiments in China and France. Use data for GWAS, genomic prediction, trait–yield correlations, and model parameterization.
SO 4 – Train and disseminate
Organize researcher exchanges, courses, and seminars for students and scientists from both institutions. Promote FAIR data management and strengthen international cooperation.
Participating scientists
INRAE, UMR LEPSE: Dr. Pierre Martre (INRAE coordinator), 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 coordinator), Prof. Shouyang Liu (NAU executive coordinator), 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 (na) Improving the prediction of phenotypic variability among genotypes through the assimilation of high-throughput phenotyping observations into crop growth model. Sumbitted.
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
