Crop pest image classification based on improved densely connected convolutional network.

Peng, Hongxing; Xu, Huiming; Gao, Zongmei; Zhou, Zhiyan; Tian, Xingguo; Deng, Qianting; He, Huijun; Xian, Chunlong

Peng, Hongxing; Xu, Huiming; Gao, Zongmei; Zhou, Zhiyan; Tian, Xingguo; Deng, Qianting; He, Huijun; Xian, Chunlong.

Afiliação

Peng H; College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China.
Xu H; Key Laboratory of Smart Agricultural Technology in Tropical South China, Ministry of Agriculture and Rural Affairs, Guangzhou, China.
Gao Z; College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China.
Zhou Z; Center for Precision and Automated Agricultural Systems, Department of Biological Systems Engineering, Washington State University, Prosser, WA, United States.
Tian X; College of Engineering, South China Agricultural University, Guangzhou, China.
Deng Q; College of Food Science, South China Agricultural University, Guangzhou, China.
He H; Department of Asset and Laboratory Management, South China Agricultural University, Guangzhou, China.
Xian C; College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China.

Front Plant Sci ; 14: 1133060, 2023.

Article em En | MEDLINE | ID: mdl-37077629

Palavras-chave

DenseNet-121; ensemble learning; pest image classification; representative batch normalization; selective kernel unit

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Front Plant Sci Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China

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