DMN-YOLO: A Robust YOLOv11 Model for Detecting Apple Leaf Diseases in Complex Field Conditions

Lijun Gao, Hongwu Cao, Hua Zou and Huanhuan Wu ()
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Lijun Gao: College of Information Engineering, Tarim University, City of Aral 843300, China
Hongwu Cao: College of Cyber Security, Tarim University, City of Aral 843300, China
Hua Zou: School of Computer Science, Wuhan University, Wuhan 430072, China
Huanhuan Wu: College of Information Engineering, Tarim University, City of Aral 843300, China

Agriculture, 2025, vol. 15, issue 11, 1-26

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.

Keywords: apple leaf diseases; intelligent agriculture; MAFPN; RT-DETR; normalized Wasserstein distance loss; YOLOv11 (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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