CSXAI: a lightweight 2D CNN-SVM model for detection and classification of various crop diseases with explainable AI visualization.

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作者： Reazul Hasan,Prince ^[1] ; Abdul Al,Mamun ^[2] ; Hasibul Islam,Peyal ^[3] ; Shafiun,Miraz ^[4] ; Md,Nahiduzzaman ^[1] ; Amith,Khandakar ^[5] ; Mohamed Arselene,Ayari ^[6]

作者单位： Department of Electrical and Computer Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh. ^[1] Department of Computer Science and Engineering, Tejgaon College, Dhaka, Bangladesh. ^[2] Department of Electrical and Computer Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh.;Department of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh. ^[3] Department of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh. ^[4] Department of Electrical Engineering, College of Engineering, Qatar University, Doha, Qatar. ^[5] Department of Civil and Architectural Engineering, Qatar University, Doha, Qatar.;Technology Innovation and Engineering Education Unit, Qatar University, Doha, Qatar. ^[6]

关键词 convolutional neural network (CNN)gradient-weighted class activation mapping (GRAD-CAM)plant diseases pre-trained models support vector machine (SVM)

DOI 10.3389/fpls.2024.1412988

PMID 39036360

发布时间 2024-07-22

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Frontiers in plant science

2024年15卷

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CSXAI: a lightweight 2D CNN-SVM model for detection and classification of various crop diseases with explainable AI visualization.

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CSXAI: a lightweight 2D CNN-SVM model for detection and classification of various crop diseases with explainable AI visualization.

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