High Throughput Field Phenotyping for Plant Height Using UAV-Based RGB Imagery in Wheat Breeding Lines: Feasibility and Validation.

Volpato, Leonardo; Pinto, Francisco; González-Pérez, Lorena; Thompson, Iyotirindranath Gilberto; Borém, Aluízio; Reynolds, Matthew; Gérard, Bruno; Molero, Gemma; Rodrigues, Francelino Augusto

Volpato, Leonardo; Pinto, Francisco; González-Pérez, Lorena; Thompson, Iyotirindranath Gilberto; Borém, Aluízio; Reynolds, Matthew; Gérard, Bruno; Molero, Gemma; Rodrigues, Francelino Augusto.

Afiliação

Volpato L; Department of Agronomy, Federal University of Viçosa, Viçosa, Brazil.
Pinto F; International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico.
González-Pérez L; International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico.
Thompson IG; International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico.
Borém A; Department of Agronomy, Federal University of Viçosa, Viçosa, Brazil.
Reynolds M; International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico.
Gérard B; International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico.
Molero G; International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico.
Rodrigues FA; KWS Momont Recherche, Mons-en-Pevele, France.

Front Plant Sci ; 12: 591587, 2021.

Article em En | MEDLINE | ID: mdl-33664755

Palavras-chave

RGB camera; adjusted and predicted genotypic values; dense point cloud; drones; multi-temporal crop surface model; post-processed kinematic; structure from motion; wheat breeding

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Front Plant Sci Ano de publicação: 2021 Tipo de documento: Article

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