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YOLO-YSTs: An Improved YOLOv10n-Based Method for Real-Time Field Pest Detection

文献类型： 外文期刊

作者： Huang, Yiqi 1 ; Liu, Zhenhao 1 ; Zhao, Hehua 3 ; Tang, Chao 4 ; Liu, Bo 2 ; Li, Zaiyuan 2 ; Wan, Fanghao 2 ; Qian, Wanqiang 2 ; Qiao, Xi 2 ;

作者机构： 1.Guangxi Univ, Coll Mech Engn, Nanning 530004, Peoples R China

2.Chinese Acad Agr Sci, Agr Genom Inst Shenzhen, Guangdong Lab Lingnan Modern Agr, Shenzhen Branch,Genome Anal Lab,Minist Agr & Rural, Shenzhen 518120, Peoples R China

3.Qingdao Engn Vocat Coll, Coll Mech & Elect Engn, Qingdao 266112, Peoples R China

4.Chinese Acad Trop Agr Sci, Environm & Plant Protect Res Inst, Haikou 571701, Peoples R China

关键词： yellow sticky traps; pest detection; Yolov10n; lightweight; edge computing platforms

期刊名称：AGRONOMY-BASEL （ 影响因子：3.4； 五年影响因子：3.8 ）

ISSN：

年卷期： 2025 年 15 卷 3 期

页码：

收录情况： SCI

摘要： The use of yellow sticky traps is a green pest control method that utilizes the pests' attraction to the color yellow. The use of yellow sticky traps not only controls pest populations but also enables monitoring, offering a more economical and environmentally friendly alternative to pesticides. However, the small size and dense distribution of pests on yellow sticky traps lead to lower detection accuracy when using lightweight models. On the other hand, large models suffer from longer training times and deployment difficulties, posing challenges for pest detection in the field using edge computing platforms. To address these issues, this paper proposes a lightweight detection method, YOLO-YSTs, based on an improved YOLOv10n model. The method aims to balance pest detection accuracy and model size and has been validated on edge computing platforms. This model incorporates SPD-Conv convolutional modules, the iRMB inverted residual block attention mechanism, and the Inner-SIoU loss function to improve the YOLOv10n network architecture, ultimately addressing the issues of missed and false detections for small and overlapping targets while balancing model speed and accuracy. Experimental results show that the YOLO-YSTs model achieved precision, recall, mAP50, and mAP50-95 values of 83.2%, 83.2%, 86.8%, and 41.3%, respectively, on the yellow sticky trap dataset. The detection speed reached 139 FPS, with GFLOPs at only 8.8. Compared with the YOLOv10n model, the mAP50 improved by 1.7%. Compared with other mainstream object detection models, YOLO-YSTs also achieved the best overall performance. Through improvements to the YOLOv10n model, the accuracy of pest detection on yellow sticky traps was effectively enhanced, and the model demonstrated good detection performance when deployed on edge mobile platforms. In conclusion, the proposed YOLO-YSTs model offers more balanced performance in the detection of pest images on yellow sticky traps. It performs well when deployed on edge mobile platforms, making it of significant importance for field pest monitoring and integrated pest management.

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