A Multimodal Parallel Transformer Framework for Apple Disease Detection and Severity Classification with Lightweight Optimization

One of the world’s most important economic crops, apples face numerous disease threats during their production process, posing significant challenges to orchard management and yield quality. To address the impact of complex disease characteristics and diverse environmental factors on detection accuracy, this study proposes a multimodal parallel transformer-based approach for apple disease detection and classification. By integrating multimodal data fusion and lightweight optimization techniques, the proposed method significantly enhances detection accuracy and robustness. Experimental results demonstrate that the method achieves an accuracy of 96%, precision of 97%, and recall of 94% in disease classification tasks. In severity classification, the model achieves a maximum accuracy of 94% for apple scab classification. Furthermore, the continuous frame diffusion generation module enhances the global representation of disease regions through high-dimensional feature modeling, with generated feature distributions closely aligning with real distributions. Additionally, by employing lightweight optimization techniques, the model is successfully deployed on mobile devices, achieving a frame rate of 46 FPS for efficient real-time detection. This research provides an efficient and accurate solution for orchard disease monitoring and lays a foundation for the advancement of intelligent agricultural technologies.