AN OPTIMIZED ATTENTION-BASED DEEP LEARNING MODEL FOR BLACK GRAM LEAF DISEASE CLASSIFICATION

Black gram, a critical pulse crop that accounts for over 70% of global production, is essential for economicstability and nutritional security. Leaf diseases, including Anthracnose, Powdery Mildew, Leaf Crinkle, andYellow Mosaic, severely jeopardize its productivity, resulting in substantial crop losses. In order to resolvethese obstacles, this investigation suggests an automated deep learning-based solution for the early detectionand classification of diseases. The research introduces the Efficient AttentionNET model, which isincorporated with Channel Attention and Spatial Attention mechanisms to improve feature extraction, usingthe Black Gram Plant Leaf Disease Dataset (BPLD) that includes in-field images. The model was able toeffectively acquire critical edge information by utilizing wavelet-transformed samples for data augmentation.The SVM classifier with an RBF kernel demonstrated exceptional performance, achieving a 99.50% F1-score, 99.50% precision, 99.52% recall, and 99.50% accuracy. The proposed model is highly effective in theclassification of black gram leaf disease due to the integration of wavelet-based augmentation and attentionmechanisms. This innovative approach enhances agricultural disease management by assisting farmers in thereduction of yield losses and the promotion of sustainable farming practices. Consequently, it contributes toglobal food security and economic resilience.