Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava (<i>Manihot esculenta</i> Crantz).

Selvaraj, Michael Gomez; Valderrama, Manuel; Guzman, Diego; Valencia, Milton; Ruiz, Henry; Acharjee, Animesh

Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava (Manihot esculenta Crantz).

Selvaraj, Michael Gomez; Valderrama, Manuel; Guzman, Diego; Valencia, Milton; Ruiz, Henry; Acharjee, Animesh.

Afiliação

Selvaraj MG; International Center for Tropical Agriculture (CIAT), A.A. 6713 Cali, Colombia.
Valderrama M; International Center for Tropical Agriculture (CIAT), A.A. 6713 Cali, Colombia.
Guzman D; International Center for Tropical Agriculture (CIAT), A.A. 6713 Cali, Colombia.
Valencia M; International Center for Tropical Agriculture (CIAT), A.A. 6713 Cali, Colombia.
Ruiz H; Department of Soil and Crop Sciences, Texas A&M University, College Station, TX USA.
Acharjee A; College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, Birmingham, B15 2TT UK.

Plant Methods ; 16: 87, 2020.

Article em En | MEDLINE | ID: mdl-32549903

Palavras-chave

Above-ground biomass; Automated aerial image processing; Cassava; Machine learning; Multispectral UAV imagery; Root yield prediction

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Plant Methods Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Colômbia País de publicação: Reino Unido

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