Evaluation of Effective Class-Balancing Techniques for CNN-Based Assessment of Aphanomyces Root Rot Resistance in Pea (<i>Pisum sativum</i> L.).

Divyanth, L G; Marzougui, Afef; González-Bernal, Maria Jose; McGee, Rebecca J; Rubiales, Diego; Sankaran, Sindhuja

Evaluation of Effective Class-Balancing Techniques for CNN-Based Assessment of Aphanomyces Root Rot Resistance in Pea (Pisum sativum L.).

Divyanth, L G; Marzougui, Afef; González-Bernal, Maria Jose; McGee, Rebecca J; Rubiales, Diego; Sankaran, Sindhuja.

Afiliação

Divyanth LG; Department of Biological Systems Engineering, Washington State University, Pullman, WA 99164, USA.
Marzougui A; Department of Agricultural and Food Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, India.
González-Bernal MJ; Department of Biological Systems Engineering, Washington State University, Pullman, WA 99164, USA.
McGee RJ; The Institute for Sustainable Agriculture, Spanish National Research Council, 14001 Cordova, Spain.
Rubiales D; Grain Legume Genetics and Physiology Research Unit, US Department of Agriculture-Agricultural Research Service (USDA-ARS), Pullman, WA 99164, USA.
Sankaran S; The Institute for Sustainable Agriculture, Spanish National Research Council, 14001 Cordova, Spain.

Sensors (Basel) ; 22(19)2022 Sep 24.

Article em En | MEDLINE | ID: mdl-36236336

Assuntos

Aphanomyces; Aphanomyces/genética; Resistência à Doença/genética; Genótipo; Pisum sativum

Palavras-chave

deep learning; disease identification; generative adversarial networks; plant breeding

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Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Aphanomyces Tipo de estudo: Prognostic_studies Idioma: En Revista: Sensors (Basel) Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos

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