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

DeSalvio, Aaron J; Adak, Alper; Murray, Seth C; Wilde, Scott C; Isakeit, Thomas

DeSalvio, Aaron J; Adak, Alper; Murray, Seth C; Wilde, Scott C; Isakeit, Thomas.

Afiliação

DeSalvio AJ; Department of Biochemistry & Biophysics, Texas A&M University, College Station, TX, 77843-2128, USA.
Adak A; Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, 77843-2474, USA.
Murray SC; Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, 77843-2474, USA. sethmurray@tamu.edu.
Wilde SC; Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, 77843-2474, USA.
Isakeit T; Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, 77843-2474, USA.

Sci Rep ; 12(1): 7571, 2022 05 09.

Article em En | MEDLINE | ID: mdl-35534655

Assuntos

Basidiomycota; Zea mays; Algoritmos; Aprendizado de Máquina; Fenômica; Zea mays/genética

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Basidiomycota / Zea mays Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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