A novel residual learning-based deep learning model integrated with attention mechanism and SVM for identifying tea plant diseases

Tea is one of the most valuable crops in many tea-producing countries. However, tea plants are vulnerable to various diseases, which reduce tea production. Early diagnosis of diseases is crucial to averting their detrimental effects on the growth and quality of tea. Conventional disease identification methods depend on the manual analysis of disease features by experts, which is time-consuming and resource-intensive. Moreover, published approaches based on computer vision left a broad scope for improving accuracy and reducing computational costs. This work attempts to design an automated learning-based model by leveraging the power of deep learning methods with reduced computational costs for accurately identifying tea diseases. The proposed work uses a Convolutional Neural Network architecture based on depthwise separable convolutions and residual networks integrated with a Support Vector Machine. Additionally, an attention module is added to the model for precise extraction of disease features. An image dataset is constructed comprising the images of healthy and diseased tea leaves infected with blister blight, grey blight, and red rust. The performance of the proposed model is evaluated on the self-generated tea dataset and compared with eight other state-of-the-art deep-learning models to establish its significance. The model achieves an overall accuracy of 99.28%.