Detection method of wheat spike improved YOLOv5s based on the attention mechanism.

Zang, Hecang; Wang, Yanjing; Ru, Linyuan; Zhou, Meng; Chen, Dandan; Zhao, Qing; Zhang, Jie; Li, Guoqiang; Zheng, Guoqing

Zang, Hecang; Wang, Yanjing; Ru, Linyuan; Zhou, Meng; Chen, Dandan; Zhao, Qing; Zhang, Jie; Li, Guoqiang; Zheng, Guoqing.

Afiliación

Zang H; Institute of Agricultural Economics and Information, Henan Academy of Agricultural Sciences, Zhengzhou, China.
Wang Y; Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs, Zhengzhou, China.
Ru L; College of Life Sciences, Zhengzhou Normal University, Zhengzhou, China.
Zhou M; College of Computer and Information Engineering, Henan Normal University, Xinxiang, China.
Chen D; Institute of Agricultural Economics and Information, Henan Academy of Agricultural Sciences, Zhengzhou, China.
Zhao Q; Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs, Zhengzhou, China.
Zhang J; Institute of Agricultural Economics and Information, Henan Academy of Agricultural Sciences, Zhengzhou, China.
Li G; Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs, Zhengzhou, China.
Zheng G; Institute of Agricultural Economics and Information, Henan Academy of Agricultural Sciences, Zhengzhou, China.

Front Plant Sci ; 13: 993244, 2022.

Article en En | MEDLINE | ID: mdl-36247573

Palabras clave

YOLOv5s; attention mechanism; deep learning; spike number detection; wheat

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Front Plant Sci Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza

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