Team Digiplante

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Section: Scientific Foundations

Model Evaluation from Experimental Data

This point can be considered as central in the DIGIPLANTE project.

Parametric Identification : estimation of model parameters and evaluation of estimation uncertainty

Model selection

In plant growth modeling, it seems that each research group is developing its own model. It is thus crucial to compare, conceptually and mathematically, the existing models, in order to assess their differences and select the 'best' models regarding specific objectives. Therefore, several classical models (STICS, PILOTE, ADEL-NEMA, SUNFLO / CORNFLO ...) are also considered in IPANEMA beside the GreenLab model. Our objective is to test different selection criteria, particularly MDL (Minimum Description Length) in collaboration with L2S Supélec-CNRS and MSEP (mean-square error of prediction).

Optimization of experimental protocol for phenotyping

If we obtain a good estimation of the uncertainty in model parameters (that is the objective of the research axis described in 3.2.1 ), we will also be able to optimize the experimental protocols. This is particularly important in phenotyping for seed companies, that need to evaluate the performances of large numbers of new varieties each year. The optimization concerns the amount of data to collect in a given experimental situation, and the number of experimental situations (with respect to climatic scenarios). The PhD of Fenni Kang studies these questions, in collaboration with J. Lecoeur (Syngenta).

Data acquisition from aerial images and data assimilation

Using real data is the key to decrease model uncertainty. For this purpose, aerial (or satellite, or drone) images provide a very interesting source of information. A new researcher (Ingénieur Confirmé) in the group Corina Iovan is a specialist of image analysis for vegetation. The objective is to assimilate this data, in order to: