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

被引:0
作者
Zhou, Siyi [1 ]
Yin, Wenjie [1 ]
He, Yinghao [1 ]
Kan, Xu [2 ]
Li, Xin [3 ]
机构
[1] Dalian Univ Technol, City Inst, Elect & Automat Coll, Dalian 116600, Peoples R China
[2] Dalian Univ Technol, Sch Comp Sci & Technol, Dalian 116024, Peoples R China
[3] Dalian East Patent Agent Ltd, Dalian 116014, Peoples R China
关键词
improved YOLOv8 network; small object detection; gray spot disease; deep learning;
D O I
10.3390/math13050840
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
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.
引用
收藏
页数:15
相关论文
共 37 条
[11]   Image features based intelligent apple disease prediction system: Machine learning based apple disease prediction system [J].
Jan M. ;
Ahmad H. .
International Journal of Agricultural and Environmental Information Systems, 2020, 11 (03) :31-47
[12]   Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks [J].
Jiang, Peng ;
Chen, Yuehan ;
Liu, Bin ;
He, Dongjian ;
Liang, Chunquan .
IEEE ACCESS, 2019, 7 :59069-59080
[13]   Learning lightweight super-resolution networks with weight pruning [J].
Jiang, Xinrui ;
Wang, Nannan ;
Xin, Jingwei ;
Xia, Xiaobo ;
Yang, Xi ;
Gao, Xinbo .
NEURAL NETWORKS, 2021, 144 :21-32
[14]  
Khan Altaf, 2021, In Silico Pharmacology, V10, P1, DOI [10.1007/s40203-021-00116-8, 10.1504/IJCISTUDIES.2021.113831]
[15]   An improved YOLOv5-based vegetable disease detection method [J].
Li, Jiawei ;
Qiao, Yongliang ;
Liu, Sha ;
Zhang, Jiaheng ;
Yang, Zhenchao ;
Wang, Meili .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2022, 202
[16]   A multi-scale cucumber disease detection method in natural scenes based on YOLOv5 [J].
Li, Shufei ;
Li, Kaiyu ;
Qiao, Yan ;
Zhang, Lingxian .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2022, 202
[17]  
[李想 Li Xiang], 2023, [农业工程学报, Transactions of the Chinese Society of Agricultural Engineering], V39, P184
[18]   Occluded Pedestrian Detection Algorithm Based on Improved YOLOv3 [J].
Li Xiang ;
He Miao ;
Luo Haibo .
ACTA OPTICA SINICA, 2022, 42 (14)
[19]   Grape Leaf Disease Identification Using Improved Deep Convolutional Neural Networks [J].
Liu, Bin ;
Ding, Zefeng ;
Tian, Liangliang ;
He, Dongjian ;
Li, Shuqin ;
Wang, Hongyan .
FRONTIERS IN PLANT SCIENCE, 2020, 11
[20]   Receptive Field Block Net for Accurate and Fast Object Detection [J].
Liu, Songtao ;
Huang, Di ;
Wang, Yunhong .
COMPUTER VISION - ECCV 2018, PT XI, 2018, 11215 :404-419