Teuku Rizky Noviandy, Aga Maulana, Faris Khowarizmi, Kahlil Muchtar
Global agriculture faces a major threat from plant diseases, particularly those affecting bean leaves, resulting in significant crop yield losses and impacting farmers' livelihoods and food production. To overcome the limitations of manual visual inspection methods, researchers have adopted computer vision and deep learning techniques for automated disease detection. This study improves the accuracy of classifying bean leaf diseases using Contrast Limited Adaptive Histogram Equalization (CLAHE) and the EfficientNetB0 architecture. Explainable AI technique, specifically Grad-CAM, is employed to analyze predicted images for transparency and interpretability. The proposed approach outperforms models trained on original images, achieving higher accuracy, precision, recall, and F1 score. CLAHE proves to be effective in optimizing CNN-based models for plant disease detection. Grad-CAM analysis reveals the model's focus on disease-related features, enhancing understanding of its behavior. This research contributes to the development of robust automated systems for plant disease detection, benefiting farmers and agricultural practitioners in decision-making. © 2023 IEEE.
Universitas Syiah Kuala, Artificial Intelligence, Department of Informatics, Aceh, Indonesia; Universitas Syiah Kuala Telematics Research Center (TRC), Universitas Syiah Kuala, Department of Electrical and Computer Engineering, Aceh, Indonesia
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