Seth C. Murray

This paper introduces a regression-based framework to predict flowering time from sparse UAV observations, reducing the need for repeated data collection.

Drone imagery from 356 flights in 19 Genomes to Fields maize environments; vegetation-index functional principal components complement genomic kernels, predicting yield of held-out hybrids at r up to 0.501.

The paper develops an automated image analysis pipeline for plant phenotyping in controlled environments, using multispectral imaging and AI to extract and analyze phenotypic features across multiple crops.

The paper investigates how host genotype and environment interact to shape bacterial endophyte communities in maize stalks, finding that gene-by-environment interactions significantly influence microbial communities.

This paper introduces distributional data analysis to plant phenomics, showing it captures novel heritable features in cotton and maize imagery that improve yield prediction and explain genetic variation.

This paper develops a CNN-based method for high-throughput maize tasseling phenotyping using UAS imagery across multiple environments, achieving high classification accuracy.

This paper links functional data analysis with deep learning to analyze single-plant cotton senescence using temporally concatenated UAS images.

The paper introduces an R function to create shapefiles for remote sensing data extraction in agricultural research plots.