A Novel TwinNet Transformer-Based Deep Learning Model for Accurate and Efficient Paddy Leaf Disease Diagnosis Using Explainable AI

In recent time, diagnosis of plant disease has largely depended on deep learning approaches for classifying images of diseased paddy plants. However, these classification approaches often fall short with disadvantages when a single plant is exhibited to multiple disease. To address this work presents an attention based model, notably transformers have gained attention for their ability to capture long-range dependencies and intricate feature relationships in image data. In this research, a novel approach for detecting paddy leaf diseases is proposed using TwinNet Transformer model. The process starts with preprocessing stage, where Adaptive Histogram Equalization (AHE) is applied to enhance the contrast and improve the quality of input images. Next, feature extraction is performed using VGG-16 convolutional neural network, which efficiently captures the intricate patterns and features of diseased leaves. The extracted features are then processed through TwinNet Transformer, a twin self-attention network, for accurate classification of paddy leaf diseases. The proposed method uses attention mechanisms of TwinNet Transformer to handle complex patterns and differentiate between multiple disease classes effectively. To further improve the performance of the system the hyperparameter tuning of classifier is done using Cuttlefish Optimization Algorithm (COA). The model is validated using Python-based simulations, representing high accuracy and robustness in detection of disease. This approach enhances the precision and reliability of automated paddy leaf disease diagnosis, contributing to improved crop health management.