Scott Cameron Chapman

The paper develops a hierarchical Bayesian model to analyze dynamic phenotypes in multi-environment trials, quantifying genotype, environment, and G x E effects on growth parameters.

A 1,096-image pixel-labelled wheat organ dataset plus 52,078 unlabelled field images from 11 institutions enables Segformer leaf and spike segmentation near 90% mIoU, while stems lag.

This paper uses UAV phenotyping and machine learning to predict barley root traits in the field, identifying genetic drivers of root gravitropism.

The paper explores using crop models as gene-to-phenotype link functions to optimize selection in plant breeding programs by integrating quantitative genetic theory and trait modeling.