High-speed identification system for fresh tea leaves based on phenotypic characteristics utilizing an improved genetic algorithm.

Gan, Ning; Sun, Mufang; Lu, Chengye; Li, Menghui; Wang, Yujie; Song, Yan; Ning, Jing-Ming; Zhang, Zheng-Zhu

Gan, Ning; Sun, Mufang; Lu, Chengye; Li, Menghui; Wang, Yujie; Song, Yan; Ning, Jing-Ming; Zhang, Zheng-Zhu.

Afiliación

Gan N; State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China.
Sun M; State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China.
Lu C; State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China.
Li M; State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China.
Wang Y; State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China.
Song Y; College of Engineering, Anhui Agricultural University, Hefei, China.
Ning JM; State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China.
Zhang ZZ; State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China.

J Sci Food Agric ; 102(15): 6858-6867, 2022 Dec.

Article en En | MEDLINE | ID: mdl-35654754

Asunto(s)

Algoritmos; Máquina de Vectores de Soporte; Teorema de Bayes; Hojas de la Planta; Té

Palabras clave

feature selection; fresh tea leaves; genetic algorithm; identification; phenotypic characteristics

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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Algoritmos / Máquina de Vectores de Soporte Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: J Sci Food Agric Año: 2022 Tipo del documento: Article País de afiliación: China

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