Detection of Apple Leaf Gray Spot Disease Based on Improved YOLOv8 Network

Siyi Zhou, Wenjie Yin, Yinghao He (), Xu Kan () and Xin Li
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Siyi Zhou: Electronics and Automation College, City Institute, Dalian University of Technology, Dalian 116600, China
Wenjie Yin: Electronics and Automation College, City Institute, Dalian University of Technology, Dalian 116600, China
Yinghao He: Electronics and Automation College, City Institute, Dalian University of Technology, Dalian 116600, China
Xu Kan: School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
Xin Li: Dalian East Patent Agent Ltd., Dalian 116014, China

Mathematics, 2025, vol. 13, issue 5, 1-15

Abstract: In the realm of apple cultivation, the efficient and real-time monitoring of Gray Leaf Spot is the foundation of the effective management of pest control, reducing pesticide dependence and easing the burden on the environment. Additionally, it promotes the harmonious development of the agricultural economy and ecological balance. However, due to the dense foliage and diverse lesion characteristics, monitoring the disease faces unprecedented technical challenges. This paper proposes a detection model for Gray Leaf Spot on apple, which is based on an enhanced YOLOv8 network. The details are as follows: (1) we introduce Dynamic Residual Blocks (DRBs) to boost the model’s ability to extract lesion features, thereby improving detection accuracy; (2) add a Self-Balancing Attention Mechanism (SBAY) to optimize the feature fusion and improve the ability to deal with complex backgrounds; and (3) incorporate an ultra-small detection head and simplify the computational model to reduce the complexity of the YOLOv8 network while maintaining the high precision of detection. The experimental results show that the enhanced model outperforms the original YOLOv8 network in detecting Gray Leaf Spot. Notably, when the Intersection over Union (IoU) is 0.5, an improvement of 7.92% in average precision is observed. Therefore, this advanced detection technology holds pivotal significance in advancing the sustainable development of the apple industry and environment-friendly agriculture.

Keywords: improved YOLOv8 network; small object detection; gray spot disease; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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