Plant Leaf Disease and Severity Classification with Improved PCNN-Bi-GRU Architecture with SHAP-XAI and Advanced Segmentation Model

Plant diseases have a significant effect on both the amount and quality of agricultural output. The majority of these illnesses have obvious symptoms, and visual examination of the afflicted leaves is the accepted technique for diagnosing plant leaf diseases. New diseases keep emerging as plant structures and agricultural methods change. To prevent the spread of illness and guarantee the healthy growth of crops, these diseases must be identified and classified as soon as possible. Thus, a novel Parallel Convolutional Bidirectional gated recurrent-based framework for Plant Leaf Disease and Severity Classification (PCB-PLDSC) is proposed in this research. In order to balance the dataset by producing more examples for underrepresented classes, image augmentation is done after bilateral filtering for noise reduction. Moreover, sick regions are isolated using a parallel attention-based U-Net (PA-U-Net) model. Adaptive threshold-based local gradient increasing pattern (AT-LGIP), color characteristics, and median binary pattern (MBP) are used to extract features such as texture, color, and spatial patterns. These are categorized using a hybrid parallel convolutional bidirectional gated recurrent (PCB) model, which integrates a bidirectional gated recurrent units (Bi-GRU) model for disease classification with the multi-residual and layer perceptron-based channel attention parallel convolutional neural networks (MRL-CPCNN) model. The impact of each attribute on predictions is shown during the classification phase by SHapley Additive exPlanations (SHAP)-based explainable artificial intelligence (AI). Finally, for detected diseases, the system estimates severity as mild, moderate, or severe to guide effective crop management.