Juan Manuel Herrera

Characterizing yield through wheat's perception of chronological progression: a multi-omics plant-time warping approach

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.

Explainable AI shows climate impacts on wheat yields: insights from 30 years of field data.

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.