文献类型： 外文期刊

作者： Zhang, Zhenyu ¹ ; Yang, Yunfan ¹ ; Xu, Xin ¹ ; Liu, Liangliang ¹ ; Yue, Jibo ¹ ; Ding, Ruifeng ² ; Lu, Yanhui ³ ; Liu, Jie ⁴ ; Qiao, Hongbo ¹ ;

作者机构： 1.Henan Agr Univ, Coll Informat & Management Sci, Zhengzhou 450002, Peoples R China

2.Xinjiang Acad Agr Sci, Inst Plant Protect, Urumqi 830091, Peoples R China

3.Chinese Acad Agr Sci, Inst Plant Protect, Beijing 100193, Peoples R China

4.Natl Agr Technol Serv Ctr, Div Pest Monitoring & Forecasting, Beijing 100125, Peoples R China

关键词： cotton aphid; real-time video detection; YOLOv8; edge computing

期刊名称：REMOTE SENSING （ 影响因子：4.1； 五年影响因子：4.8 ）

ISSN：

年卷期： 2024 年 16 卷 16 期

页码：

收录情况： SCI

摘要： Cotton aphids (Aphis gossypii Glover) pose a significant threat to cotton growth, exerting detrimental effects on both yield and quality. Conventional methods for pest and disease surveillance in agricultural settings suffer from a lack of real-time capability. The use of edge computing devices for real-time processing of cotton aphid-damaged leaves captured by field cameras holds significant practical research value for large-scale disease and pest control measures. The mainstream detection models are generally large in size, making it challenging to achieve real-time detection on edge computing devices with limited resources. In response to these challenges, we propose GVC-YOLO, a real-time detection method for cotton aphid-damaged leaves based on edge computing. Building upon YOLOv8n, lightweight GSConv and VoVGSCSP modules are employed to reconstruct the neck and backbone networks, thereby reducing model complexity while enhancing multiscale feature fusion. In the backbone network, we integrate the coordinate attention (CA) mechanism and the SimSPPF network to increase the model's ability to extract features of cotton aphid-damaged leaves, balancing the accuracy loss of the model after becoming lightweight. The experimental results demonstrate that the size of the GVC-YOLO model is only 5.4 MB, a decrease of 14.3% compared with the baseline network, with a reduction of 16.7% in the number of parameters and 17.1% in floating-point operations (FLOPs). The mAP@0.5 and mAP@0.5:0.95 reach 97.9% and 90.3%, respectively. The GVC-YOLO model is optimized and accelerated by TensorRT and then deployed onto the embedded edge computing device Jetson Xavier NX for detecting cotton aphid damage video captured from the camera. Under FP16 quantization, the detection speed reaches 48 frames per second (FPS). In summary, the proposed GVC-YOLO model demonstrates good detection accuracy and speed, and its performance in detecting cotton aphid damage in edge computing scenarios meets practical application needs. This research provides a convenient and effective intelligent method for the large-scale detection and precise control of pests in cotton fields.