MDGL-DETR: An Efficient Method for Detecting Apple Leaf Diseases by Integrating Global and Local Features

To address challenges such as diverse apple leaf disease phenotypes, high background similarity, and complex natural environments, this study proposes an improved MDGL-DETR model based on RT-DETR, aiming to enhance both the accuracy and efficiency of apple leaf disease detection. First, a Multi-Scale Dilated Asymmetric structure combined with Channel Reduction Attention is designed to strengthen extraction capabilities for multi-scale and global features while reducing computational costs. Second, a Directional Shift Adaptive Context module is proposed, which utilizes shift convolution and SoftPool to dynamically focus on disease regions and suppress redundant background interference. Finally, a Global-Local Collaborative Fusion module is constructed to facilitate efficient interaction between local texture and global semantic information, thereby reinforcing feature representation capabilities. Experimental results indicate that the MDGL-DETR model achieves an mAP50 of 89.09%, an increase of 3.31% over the original RT-DETR, while reducing the computational load by 4.39%. Comprehensive evaluations show that the model outperforms other object detection models. The proposed MDGL-DETR provides a novel solution for the efficient detection of apple leaf diseases.