Quantitative Extraction and Evaluation of Tomato Fruit Phenotypes Based on Image Recognition.

Zhu, Yihang; Gu, Qing; Zhao, Yiying; Wan, Hongjian; Wang, Rongqing; Zhang, Xiaobin; Cheng, Yuan

Zhu, Yihang; Gu, Qing; Zhao, Yiying; Wan, Hongjian; Wang, Rongqing; Zhang, Xiaobin; Cheng, Yuan.

Afiliação

Zhu Y; Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou, China.
Gu Q; Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou, China.
Zhao Y; Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou, China.
Wan H; State Key Laboratory for Quality and Safety of Agro-Products, Institute of Vegetables, Zhejiang Academy of Agricultural Sciences, Hangzhou, China.
Wang R; State Key Laboratory for Quality and Safety of Agro-Products, Institute of Vegetables, Zhejiang Academy of Agricultural Sciences, Hangzhou, China.
Zhang X; Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou, China.
Cheng Y; State Key Laboratory for Quality and Safety of Agro-Products, Institute of Vegetables, Zhejiang Academy of Agricultural Sciences, Hangzhou, China.

Front Plant Sci ; 13: 859290, 2022.

Article em En | MEDLINE | ID: mdl-35498696

Palavras-chave

deep learning; image recognition; phenotyping; quantitative; tomato fruit

Texto completo

Adicionar na Minha BVS

Imprimir

XML

PubMed Links

Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Guideline Idioma: En Revista: Front Plant Sci Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China País de publicação: Suíça

Texto completo

Adicionar na Minha BVS

Imprimir

XML

PubMed Links

Buscar no Google