Combining novel feature selection strategy and hyperspectral vegetation indices to predict crop yield.

Fei, Shuaipeng; Li, Lei; Han, Zhiguo; Chen, Zhen; Xiao, Yonggui

Fei, Shuaipeng; Li, Lei; Han, Zhiguo; Chen, Zhen; Xiao, Yonggui.

Afiliação

Fei S; Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang, 453002, China.
Li L; National Wheat Improvement Centre, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
Han Z; PhenoTrait Laboratory, PhenoTrait Technology Co. Ltd, Beijing, 100096, China.
Chen Z; Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang, 453002, China. chenzhen@caas.cn.
Xiao Y; National Wheat Improvement Centre, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China. xiaoyonggui@caas.cn.

Plant Methods ; 18(1): 119, 2022 Nov 08.

Article em En | MEDLINE | ID: mdl-36344997

Palavras-chave

Deep neural network; Feature selection; Hyperspectral; Vegetation index; Wheat yield

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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: 2022 Tipo de documento: Article País de afiliação: China País de publicação: Reino Unido

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