Efficient Attention Guided Fusion Model for Plant Disease Classification

Early detection of diseases in plants has been identified as a critical factor for ensuring the maintenance of productivity, preventing economic losses, and promoting sustainable agriculture. Traditional manual approaches for diagnosing diseases are time-consuming, subjective, and inappropriate for large-scale and real-time agriculture. In order to overcome the limitations of traditional approaches, the CNN–CBAM–MobileViTNet has been proposed, an efficient attention-guided network by the fusion of Convolutional Neural Networks (CNN), Convolutional Block Attention Module (CBAM), and Mobile Vision Transformer (MobileViT) for plant diseases recognition. The CNN component is effective in capturing local visual patterns like lesions, discoloration, and texture. The CBAM component is effective in refining the feature representations by focusing on diseaserelated spatial areas and useful channels. The MobileViTNet branch is useful in capturing contextual relationships from the leaf areas through lightweight transformer blocks. The CNN-CBAMMobileViTNet is tested on an enhanced dataset with 38 classes of plant diseases and health conditions, splitting data into 70% training, 15% validation, and 15% testing. Significantly, extensive experimental analysis reveals that the test accuracy is 99%, with high precision, recall, and F1-score values. Trainingvalidation curves show that the model converges stably with little overfitting, while ROC analysis show high classwise discrimination ability of the model. Hence, the CNN-CBAM-MobileViTNet model is reliable, may be used for real-world applications in smart agriculture and automatic crop disease monitoring systems.