overview

Dr. Murray's research interests focus on improving the productivity, sustainability (economic and environmental) and quality of agricultural production through scientific research and development; mostly in maize (corn). The approaches used to conduct this research include 1) high-throughput field phenotyping (UAVs/drones, ground vehicles, NIRS), 2) molecular quantitative genetic discovery (including QTL mapping, GWAS), 3) statistical modeling and novel analysis methods (including big data and metanalysis), 4) development of new breeding and genetics approaches (including use of computer simulations), and ultimately 5) applied maize (corn) field breeding (classical and molecular). Primary traits of interest for discovering genetic variation and improving in maize for are yield, southern adaptation, stress (aflatoxin resistance, drought tolerance), plant height, composition (colored grain, high grain antioxidants, low phosphorus), and perennialism. Graduate student training is deeply embedded in all of my research.

education and training
selected publications
Academic Articles76
  • Zhang, M., Liu, Y., Wang, Y., Sze, S., Scheuring, C. F., Qi, X., ... Zhang, H. (2022). Genome-wide identification of genes enabling accurate prediction of hybrid performance from parents across environments and populations for gene-based breeding in maize.. Plant Sci. 324, 111424-111424.
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  • DeSalvio, A. J., Adak, A., Murray, S. C., Wilde, S. C., & Isakeit, T. (2022). Phenomic data-facilitated rust and senescence prediction in maize using machine learning algorithms.. Sci Rep. 12(1), 7571.
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  • Weldekidan, T., Manching, H., Choquette, N., Leon, N., Flint‐Garcia, S., Holland, J., ... Wisser, R. J. (2022). Registration of tropical populations of maize selected in parallel for early flowering time across the United States. Journal of Plant Registrations. 16(1), 100-108.
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  • Lane, H. M., & Murray, S. C. (2021). High throughput can produce better decisions than high accuracy when phenotyping plant populations. Crop Science. 61(5), 3301-3313.
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  • Adak, A., Conrad, C., Chen, Y., Wilde, S. C., Murray, S. C., Anderson II, S. L., & Subramanian, N. K. (2021). Validation of functional polymorphisms affecting maize plant height by unoccupied aerial systems discovers novel temporal phenotypes. G3 Genes|Genomes|Genetics. 11(6), jkab075.
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Chapters2
  • Murray, S. C., & Wisser, R. J. (2014). Genetics, Genomics and Breeding of Maize. Wusirika, R., Bohn, M., Lai, J., & Kole, C. (Eds.), Genetics, Genomics and Breeding of Maize. 64-88. CRC Press.
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  • Murray, S. C. (2013). Differentiation of Seed, Sugar, and Biomass-Producing Genotypes in Saccharinae Species. Genomics of the Saccharinae. 479-502. Springer New York.
Conference Papers6
  • Chu, T., Starek, M. J., Brewer, M. J., & Murray, S. C. (2017). MULTI-platform uas imaging for crop height estimation: Performance analysis over an experimental maize field. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2017-July, 4338-4341.
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  • Chu, T., Starek, M. J., Brewer, M. J., Masiane, T., & Murray, S. C. (2017). UAS imaging for automated crop lodging detection: a case study over an experimental maize field. SPIE Proceedings, SPIE Commercial + Scientific Sensing and Imaging. 10218, 102180e-102180e-7.
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  • Thomasson, J. A., Shi, Y., Olsenholler, J., Valasek, J., Murray, S. C., & Bishop, M. P. (2016). Comprehensive UAV agricultural remote-sensing research at Texas A&M University. AUTONOMOUS AIR AND GROUND SENSING SYSTEMS FOR AGRICULTURAL OPTIMIZATION AND PHENOTYPING, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping. 9866, 986602-986602-7.
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  • Shi, Y., Murray, S. C., Rooney, W. L., Valasek, J., Olsenholler, J., Pugh, N. A., ... Thomasson, J. A. (2016). Corn and sorghum phenotyping using a fixed-wing UAV-based remote sensing system. AUTONOMOUS AIR AND GROUND SENSING SYSTEMS FOR AGRICULTURAL OPTIMIZATION AND PHENOTYPING, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping. 9866, 98660e-98660e-8.
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  • Murray, S. C., Knox, L., Hartley, B., Mendez-Dorado, M. A., Richardson, G., Thomasson, J. A., ... Rooney, W. L. (2016). High clearance phenotyping systems for season-long measurement of corn, sorghum and other row crops to complement unmanned aerial vehicle systems. AUTONOMOUS AIR AND GROUND SENSING SYSTEMS FOR AGRICULTURAL OPTIMIZATION AND PHENOTYPING, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping. 9866, 986607-986607-8.
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Repository Documents / Preprints3
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  • DeSalvio, A. J., Adak, A., Murray, S. C., Wilde, S. C., & Isakeit, T. (2021). Phenomic Data-Facilitated Rust and Senescence Prediction in Maize Using Machine Learning Algorithms.
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chaired theses and dissertations
Email
sethmurray@tamu.edu
First Name
Seth
Last Name
Murray
mailing address
Texas A&M University; Soil & Crop Science; 2474 TAMU
College Station, TX 77843-2474
USA