DCBLCCG NET: A Fusion Approach For Enhanced Pepper Leaf Disease Identification With Hybrid Deep Learning

The agricultural sector in developing countries like Ethiopia heavily relies on crop health and productivity. Pepper, a crucial crop for global food security, faces significant threats from various diseases, including blight leaf disease, gray leaf spot, common rust, fruit rot, and powdery mildew. The existence of 34 different pepper diseases has resulted in an average reduction of 33% in crop productivity. Traditional disease detection methods, such as visual inspection by farmers, are prone to errors and are time-consuming Previous research has largely employed image processing and deep learning for disease classification in pepper plants. This study introduces a novel approach by combining a Deep Convolutional Neural Network (DCNN) with a Bidirectional Long Short-Term Memory (BILSTM) network. While DCNN excels at extracting spatial features, BILSTM captures the sequential growth patterns of pepper plants. This hybrid model achieved $\mathbf{1 0 0 \%}$ accuracy during training and performed exceptionally well on a dataset of pepper disease images. Utilizing features from pretrained CNNs like VGG16 and AlexNet, the model attained a top classification accuracy of $\mathbf{9 9. 5 \%}$, with grayscale datasets and segmented images achieving $\mathbf{9 8. 9 \%}$ and $\mathbf{9 9. 3} \%$, respectively. This robust model is a powerful tool for rapid and accurate pepper disease identification, improving disease management and agricultural productivity.