Panicle Ratio Network: streamlining rice panicle measurement by deep learning with ultra-high-definition aerial images in the field.

Guo, Ziyue; Yang, Chenghai; Yang, Wangnen; Chen, Guoxing; Jiang, Zhao; Wang, Botao; Zhang, Jian

Guo, Ziyue; Yang, Chenghai; Yang, Wangnen; Chen, Guoxing; Jiang, Zhao; Wang, Botao; Zhang, Jian.

Afiliação

Guo Z; Macro Agriculture Research Institute, College of Resources and Environment, Huazhong Agricultural University, Wuhan, China.
Yang C; Key Laboratory of Farmland Conservation in the Middle and Lower Reaches of the Ministry of Agriculture, Wuhan, China.
Yang W; Aerial Application Technology Research Unit, USDA-Agricultural Research Service, College Station, TX, USA.
Chen G; College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China.
Jiang Z; College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China.
Wang B; Macro Agriculture Research Institute, College of Resources and Environment, Huazhong Agricultural University, Wuhan, China.
Zhang J; Key Laboratory of Farmland Conservation in the Middle and Lower Reaches of the Ministry of Agriculture, Wuhan, China.

J Exp Bot ; 73(19): 6575-6588, 2022 11 02.

Article em En | MEDLINE | ID: mdl-35776094

Assuntos

Aprendizado Profundo; Oryza; Fenótipo; Produtos Agrícolas

Palavras-chave

Deep convolutional neural network; effective tiller percentage; heading date; rice panicle ratio network; ultra-high-definition image; unmanned aerial vehicle

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Oryza / Aprendizado Profundo Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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