Characterizing yield through wheat's perception of chronological progression: a multi-omics plant-time warping approach
Lukas Roth, Juan Manuel Herrera, Lilia Levy Häner, Didier Pellet, Dario Fossati, Mike Boss, Xiaoran Chen, Paraskevi Nousi, Michele Volpi
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.
Journal of Experimental Botany · Feb 14, 2026Peer-reviewedGenomics