Didier Pellet

This paper introduces a deep learning model that integrates phenomic, genomic, and environmental data to predict wheat yield across diverse environments, showing improved performance over traditional genomic models.

The paper uses XAI and IML methods on 30 years of wheat yield data to identify climate variables affecting yields and their interactions with genotype.