Deep reinforcement learning enables adaptive-image augmentation for automated optical inspection of plant rust.

Wang, Shiyong; Khan, Asad; Lin, Ying; Jiang, Zhuo; Tang, Hao; Alomar, Suliman Yousef; Sanaullah, Muhammad; Bhatti, Uzair Aslam

Wang, Shiyong; Khan, Asad; Lin, Ying; Jiang, Zhuo; Tang, Hao; Alomar, Suliman Yousef; Sanaullah, Muhammad; Bhatti, Uzair Aslam.

Afiliación

Wang S; School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China.
Khan A; Metaverse Research Institute, School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China.
Lin Y; School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China.
Jiang Z; College of Food Science, South China Agricultural University, Guangzhou, China.
Tang H; School of Information and Communication Engineering, Hainan University, Haikou, China.
Alomar SY; Zoology Department, College of Science, King Saud University, Riyadh, Saudi Arabia.
Sanaullah M; Department of Computer Science, Bahauddin Zakariya University, Multan, Pakistan.
Bhatti UA; School of Information and Communication Engineering, Hainan University, Haikou, China.

Front Plant Sci ; 14: 1142957, 2023.

Article en En | MEDLINE | ID: mdl-37484461

Palabras clave

adaptive image augmentation; automated optical inspection; deep Q-learning; deep reinforcement learning; semantic segmentation

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Plant Sci Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza

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