Ian Stavness

The paper presents a pipeline for longitudinal plant disease phenotyping using time-series imaging, enabling objective quantification of disease progression in wheat rusts.

This paper introduces GPFN, a new genomic prediction method that outperforms linear models on multiple traits and matches their performance on a structured prediction task.

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 evaluates nonlinear methods for visualizing and inferring population structure, finding graph-based algorithms like t-SNE and UMAP outperform PCA while neural networks perform worse.

This paper shows deep neural networks for genomic prediction do not estimate marker effects, instead relying on genetic relatedness.