A dual track deep fusion network for citrus disease classification using group shuffle depthwise feature pyramid and Swin transformer

Abstract

Citrus fruits are globally significant crops, valued for their unique taste, nutritional value, and versatility in culinary applications. However, these crops are vulnerable to diseases that can substantially reduce their yield and quality, leading to significant financial losses. It is crucial to efficiently identify and manage diseases affecting citrus fruits to ensure food security and support sustainable agriculture. Traditionally, identifying symptoms associated with these diseases requires expert scientific and observational skills. With the advent of deep learning methods, it has become possible to automatically identify disease patterns from images of affected plants. In this research, a novel dual branch deep-learning network was proposed for the classification of citrus plant diseases. The Group Shuffle Depthwise Feature Pyramid (GSDFP), which constitutes the first branch, utilizes convolution blocks to extract local features. The second branch leverages the Swin transformer to integrate global contextual awareness into the extracted features and facilitates the learning process through long-term dependencies. Subsequently, the features from the two branches are fused and passed through a shuffle attention module, effectively capturing contextual relationships between them. The effectiveness of the proposed approach was validated on two benchmark datasets, namely the Citrus Plant Dataset and the Citrus Disease Image Gallery Dataset. The proposed network obtained a classification accuracy of 98.19%.