VGG-DAGSVM FOR MULTI-CLASS APPLE LEAF DISEASE

Apple cultivation is essential in world agriculture because of its high nutritional benefits and economic importance. However, increasing pressures from foliar diseases such as apple scab, cedar apple rust, and black rot seriously threaten yield and quality within UK apple orchards. One strategy is to capitalize on advanced technologies such as computer-aided diagnosis systems for improved detection and classification of diseases. To address this challenge, this paper proposes a generic and automatic solution to recognizing and classifying diseases of apple fruit leaves leveraging the VGG-DAGSVM architecture. The workflow begins with bilateral filtering to mitigate image noise without losing core edge information, and Contrast Limited Adaptive Histogram Equalization (CLACHE) for contrast enhancement. SegNet is used to accurately segment diseased regions, and the VGG-19 deep learning model features extraction. Finally, a Directed Acyclic Graph Support Vector Machine (DAGSVM) is used as the classifier for accurate disease classification. Experimental evaluation uses a publicly available dataset comprising 13,124 apple leaf images across four categories. The proposed model achieves a classification accuracy of 96.50%, precision of 96.05%, sensitivity of 95.92%, specificity of 96.43%, and F-score of 96.45%, outperforming several existing models. These results demonstrate the robustness and applicability of the potential of integrating image processing, deep feature extraction, and intelligent classification systems to support early disease detection and promise sustainable apple farming practices.