Identification and picking point positioning of tender tea shoots based on MR3P-TS model.

Yan, Lijie; Wu, Kaihua; Lin, Jia; Xu, Xingang; Zhang, Jingcheng; Zhao, Xiaohu; Tayor, James; Chen, Dongmei

Yan, Lijie; Wu, Kaihua; Lin, Jia; Xu, Xingang; Zhang, Jingcheng; Zhao, Xiaohu; Tayor, James; Chen, Dongmei.

Afiliación

Yan L; School of Automation, Hangzhou Dianzi University, Hangzhou, China.
Wu K; School of Automation, Hangzhou Dianzi University, Hangzhou, China.
Lin J; School of Automation, Hangzhou Dianzi University, Hangzhou, China.
Xu X; Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
Zhang J; School of Automation, Hangzhou Dianzi University, Hangzhou, China.
Zhao X; School of Automation, Hangzhou Dianzi University, Hangzhou, China.
Tayor J; School of Natural and Environmental Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom.
Chen D; School of Automation, Hangzhou Dianzi University, Hangzhou, China.

Front Plant Sci ; 13: 962391, 2022.

Article en En | MEDLINE | ID: mdl-36035663

Palabras clave

Camellia sinensis; deep learning; faster region-based convolutional neural network; fine tea picking; image analysis

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_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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