DMN-YOLO: A Robust YOLOv11 Model for Detecting Apple Leaf Diseases in Complex Field Conditions
Lijun Gao, Hongwu Cao, Hua Zou, Huanhuan Wu
Abstract
Accurately identifying apple leaf diseases in complex field environments is a critical concern for intelligent agriculture, as early detection directly affects crop health and yield outcomes. However, accurate feature recognition remains a significant challenge due to the complexity of disease symptoms, background interference, and variations in lesion color and size. In this study, we propose an enhanced detection framework named DMN-YOLO. Specifically, the model integrates a multi-branch auxiliary feature pyramid network (MAFPN), along with Superficial Assisted Fusion (SAF) and Advanced Auxiliary Fusion (AAF) modules, to strengthen feature interaction, retain shallow-layer information, and improve high-level gradient transmission, thereby enhancing multi-scale lesion detection performance. Furthermore, the RepHDWConv module is incorporated into the neck network to increase the model’s representational capacity. To address difficulties in detecting small and overlapping lesions, a lightweight RT-DETR decoder and a dedicated detection layer (P2) are introduced. These enhancements effectively reduce both missed and false detections. Additionally, a normalized Wasserstein distance (NWD) loss function is introduced to mitigate localization errors, particularly for small or overlapping lesions. Experimental results demonstrate that DMN-YOLO achieves a 5.5% gain in precision, a 3.4% increase in recall, and a 5.0% improvement in mAP@50 compared to the baseline, showing consistent superiority across multiple performance metrics. This method offers a promising solution for robust disease monitoring in smart orchard applications.