Search Results - DeSalvio, Aaron J

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  1. 1
    Phenomic data-facilitated rust and senescence prediction in maize using machine learning algorithms

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

    Published in Scientific reports
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  2. 2
    Near‐infrared reflectance spectroscopy phenomic prediction can perform similarly to genomic prediction of maize agronomic traits across environments

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

    Published in The plant genome
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  3. 3
    Field-based high-throughput phenotyping enhances phenomic and genomic predictions for grain yield and plant height across years in maize

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

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  4. 4
    High temporal resolution unoccupied aerial systems phenotyping provides unique information between flight dates

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

    Published in Plant phenome journal
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  5. 5
    Temporal Image Sandwiches Enable Link between Functional Data Analysis and Deep Learning for Single-Plant Cotton Senescence

    Temporal Image Sandwiches Enable Link between Functional Data Analysis and Deep Learning for Single-Plant Cotton Senescence by 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

    Published in in silico plants
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