Weed25: A deep learning dataset for weed identification.

Wang, Pei; Tang, Yin; Luo, Fan; Wang, Lihong; Li, Chengsong; Niu, Qi; Li, Hui

Wang, Pei; Tang, Yin; Luo, Fan; Wang, Lihong; Li, Chengsong; Niu, Qi; Li, Hui.

Afiliação

Wang P; Key Laboratory of Agricultural Equipment for Hilly and Mountain Areas, College of Engineering and Technology, Southwest University, Chongqing, China.
Tang Y; Key Laboratory of Modern Agricultural Equipment and Technology (Jiangsu University), Ministry of Education, School of Agricultural Engineering, Jiangsu University, Zhenjiang, China.
Luo F; Interdisciplinary Research Center for Agriculture Green Development in Yangtze River Basin, Southwest University, Chongqing, China.
Wang L; Key Laboratory of Agricultural Equipment for Hilly and Mountain Areas, College of Engineering and Technology, Southwest University, Chongqing, China.
Li C; Key Laboratory of Agricultural Equipment for Hilly and Mountain Areas, College of Engineering and Technology, Southwest University, Chongqing, China.
Niu Q; Key Laboratory of Agricultural Equipment for Hilly and Mountain Areas, College of Engineering and Technology, Southwest University, Chongqing, China.
Li H; Key Laboratory of Agricultural Equipment for Hilly and Mountain Areas, College of Engineering and Technology, Southwest University, Chongqing, China.

Front Plant Sci ; 13: 1053329, 2022.

Article em En | MEDLINE | ID: mdl-36531369

Palavras-chave

Weed25; deep learning; weed dataset; weed identification; weed species

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

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