Mike Boss

This paper uses neural processes with normalizing flows to model wheat height trajectories, evaluating their ability to capture distributional dynamics influenced by genotype and environment.

OneGrow generates temporal plant images and organ masks, addressing the lack of paired data by jointly modeling wheat images and masks over time.

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 introduces a large-scale wheat phenotyping dataset with time series imaging and trait data across six years, enabling studies on genotype-environment interactions and crop modeling.