Seth Murray

Professor and Eugene Butler Endowed Chair

Texas A&M University

United States

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About

Dr. Seth Murray is a Professor and the Eugene Butler Endowed Chair at Texas A&M University, USA. His research focuses on phenotypic plasticity, plant pathogen threats to food security, and high-throughput phenotyping in maize. Recent publications highlight his work on genetic mapping, integrative modeling of phenotype dynamics, and the application of near-infrared reflectance spectroscopy for agronomic predictions.

Articles (16)

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2025

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2022

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2022

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2022

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2022

Collaborators (15)

Matthias Katzfuß

Texas A&M University

UNITED STATES

Seth Murray

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Articles (16)

Phenotypic plasticity in maize grain yield: Genetic and environmental insights of response to environmental gradients

Detecting novel plant pathogen threats to food system security by integrating the Plant Reactome and remote sensing

Field-based high-throughput phenotyping enhances phenomic and genomic predictions for grain yield and plant height across years in maize

Near‐infrared reflectance spectroscopy phenomic prediction can perform similarly to genomic prediction of maize agronomic traits across environments

Deciphering temporal growth patterns in maize: integrative modeling of phenotype dynamics and underlying genomic variations

Temporal image sandwiches enable link between functional data analysis and deep learning for single-plant cotton senescence

Identification of naturally occurring atoxigenic strains of Fusarium verticillioides and their potential as biocontrol agents of mycotoxins and ear rot pathogens of maize

Phenomic data-driven biological prediction of maize through field-based high-throughput phenotyping integration with genomic data

Genetic mapping and prediction for novel lesion mimic in maize demonstrates quantitative effects from genetic background, environment and epistasis

Environment‐specific selection alters flowering‐time plasticity and results in pervasive pleiotropic responses in maize

Temporal phenomic predictions from unoccupied aerial systems can outperform genomic predictions

Pedigree‐management‐flight interaction for temporal phenotype analysis and temporal phenomic prediction

Genome-wide identification of genes enabling accurate prediction of hybrid performance from parents across environments and populations for gene-based breeding in maize

Genetic variation in hydrogen cyanide potential of perennial sorghum evaluated by colorimetry

Root system size and root hair length are key phenes for nitrate acquisition and biomass production across natural variation in Arabidopsis

Phenomic data-facilitated rust and senescence prediction in maize using machine learning algorithms

Collaborators (15)

Matthias Katzfuß

Diego Jarquin

Sushma Naithani

William Rooney

Michael Gore

Raymond K. W. Wong

Candice N Hirsch

David Stelly

Aart Verhoef

Leo Hoffmann

Eli Borrego

Randall J. Wisser

Michael Kolomiets

Maninder Pal Singh

James B. Holland

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