An effective deep learning approach for the classification of Bacteriosis in peach leave.

Akbar, Muneer; Ullah, Mohib; Shah, Babar; Khan, Rafi Ullah; Hussain, Tariq; Ali, Farman; Alenezi, Fayadh; Syed, Ikram; Kwak, Kyung Sup

Akbar, Muneer; Ullah, Mohib; Shah, Babar; Khan, Rafi Ullah; Hussain, Tariq; Ali, Farman; Alenezi, Fayadh; Syed, Ikram; Kwak, Kyung Sup.

Afiliación

Akbar M; Institute of Computer Science & Information Technology, The University of Agriculture, Peshawar, Pakistan.
Ullah M; Institute of Computer Science & Information Technology, The University of Agriculture, Peshawar, Pakistan.
Shah B; College of Technological Innovation, Zayed University, Dubai, United Arab Emirates.
Khan RU; Institute of Computer Science & Information Technology, The University of Agriculture, Peshawar, Pakistan.
Hussain T; High Performance Computing and Networking Institute, National Research Council (ICAR-CNR), Naples, Italy.
Ali F; Department of Software, Sejong University, Seoul, South Korea.
Alenezi F; Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka, Saudi Arabia.
Syed I; School of Computing, Gachon University, Seongnam-Si, South Korea.
Kwak KS; Department of Information and Communication Engineering, Inha University, Incheon, South Korea.

Front Plant Sci ; 13: 1064854, 2022.

Article en En | MEDLINE | ID: mdl-36507379

Palabras clave

Bacteriosis classification; Bacteriosis detection; LWNet; convolutional neural network (CNN); deep learning; peach leaves

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Plant Sci Año: 2022 Tipo del documento: Article País de afiliación: Pakistán Pais de publicación: Suiza

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