Phenomic data-facilitated rust and senescence prediction in maize using machine learning algorithms von DeSalvio, Aaron J., Adak, Alper, Murray, Seth C., Wilde, Scott C., Isakeit, Thomas

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Near‐infrared reflectance spectroscopy phenomic prediction can perform similarly to genomic prediction of maize agronomic traits across environments von DeSalvio, Aaron J., Adak, Alper, Murray, Seth C., Jarquín, Diego, Winans, Noah D., Crozier, Daniel, Rooney, William L.

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Field-based high-throughput phenotyping enhances phenomic and genomic predictions for grain yield and plant height across years in maize von Adak, Alper, DeSalvio, Aaron J, Arik, Mustafa A, Murray, Seth C

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High temporal resolution unoccupied aerial systems phenotyping provides unique information between flight dates von Washburn, Jacob D., Adak, Alper, DeSalvio, Aaron J., Arik, Mustafa A., Murray, Seth C.

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Temporal Image Sandwiches Enable Link between Functional Data Analysis and Deep Learning for Single-Plant Cotton Senescence von DeSalvio, Aaron J, Adak, Alper, Arik, Mustafa A, Shepard, Nicholas R, DeSalvio, Serina M, Murray, Seth C, García-Ramos, Oriana, Badavath, Himabindhu, Stelly, David M

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Supplemental Material for Adak et al., 2023 von Adak, Alper, J. DeSalvio, Aaron, A. Arik, Mustafa, Murray, Seth C.

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