PRMT: Retentive Networks Meet Vision Transformers for Plant Disease Identification

Accurate and rapid classification of plant diseases is crucial for enhancing productivity in contemporary agriculture. Both modern deep-learning models and conventional methods encounter obstacles when it comes to finding plant diseas-es. For instance, complicated scenarios often increase processing costs and re-duce recognition accuracy. This study introduces the PRMT Retentive Networks Meet Vision Transformers for Plant Disease Identification framework, utilizing the Retentive Networks Meet Vision Transformers RMT architecture. The method utilizes Manhattan distances and spatial prior knowledge to create a spa-tial attenuation matrix. It improves internal correlations and enables a greater un-derstanding of the relationships among image regions. The design incorporates the Convolutional Block Attention Module CBAM to enhance feature represen-tations. Incorporating 2D average pooling in the backbone network diminishes sensitivity to local noise and inhibits an increase in model parameters. We em-ployed datasets on paddy, corn, wheat, and coffee diseases. To enhance the utili-zation of the datasets, we implemented rotation, scaling, and color modification and conducted three-fold cross-validation. We assessed the PRMT model is per-formance using recall, specificity, accuracy, and precision metrics and compared it with other models. Studies show that the PRMT model can easily handle big and complicated datasets of agricultural diseases, leading to much better results with only a few extra parameters. Our methodology improves the effectiveness of categorizing intricate plant disease images.