[1]施杰,熊凯祥,李志,等.基于轻量化改进YOLOv8模型和边缘计算的玉米病虫害检测系统[J].江苏农业学报,2025,(02):313-322.[doi:doi:10.3969/j.issn.1000-4440.2025.02.012]
　SHI Jie,XIONG Kaixiang,LI Zhi,et al.Maize pest and disease detection system based on lightweight improved YOLOv8 model and edge computing[J].,2025,(02):313-322.[doi:doi:10.3969/j.issn.1000-4440.2025.02.012]
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基于轻量化改进YOLOv8模型和边缘计算的玉米病虫害检测系统()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2025年02期
页码:
313-322
栏目:
农业信息工程
出版日期:
2025-02-28

文章信息/Info

Title:
Maize pest and disease detection system based on lightweight improved YOLOv8 model and edge computing
作者:
施杰1; 2; 熊凯祥1; 李志1; 陈立畅1; 唐秀英1; 杨琳琳1; 2
(1.云南农业大学机电工程学院,云南昆明650201；2.云南省作物生产与智慧农业重点实验室,云南昆明650201)
Author(s):
SHI Jie1; 2; XIONG Kaixiang1; LI Zhi1; CHEN Lichang1; TANG Xiuying1; YANG Linlin1; 2
(1.Faculty of Mechanical and Electrical Engineering, Yunnan Agricultural University, Kunming 650201, China；2.The Key Laboratory for Crop Production and Smart Agriculture of Yunnan Province, Kunming 650201, China)
关键词:
玉米; 病虫害检测系统; YOLOv8模型; 轻量化改进; 边缘计算
Keywords:
corn; pest and disease detection system; YOLOv8 model; lightweight improved model; edge computing
分类号:
S435.13
DOI:
doi:10.3969/j.issn.1000-4440.2025.02.012
文献标志码:
A
摘要:
为实现玉米病虫害的原位准确检测与识别,本研究设计了一套基于边缘计算的玉米病虫害智能检测系统。该系统基于YOLOv8模型并进行改进,具体改进方法包括:采用高效视觉网络(EfficientViT)作为主干网络,以降低计算量；在特征融合网络中引入幻影卷积(GhostConv),进一步减轻计算负担；在C2f模块中引入空间通道重建卷积(SCConv),以增强特征提取性能；将损失函数替换为具有动态非单调聚焦机制的损失函数(WIoU),以提高模型的识别精度。同时,本研究设计了基于边缘计算的病虫害检测系统上位机、下位机架构,并将该轻量化模型部署到Jetson orin nano边缘计算设备上。系统采用Pyside6开发系统可视化界面,除具备识别与训练功能外,还集成了基于大模型技术的AI专家库,可以实现对病虫害的智能化诊断。通过自建的玉米病虫害数据集对改进模型YOLOv8-EGCW进行检验。结果表明,与原始模型YOLOv8m相比,改进模型YOLOv8-EGCW的精确度、召回率和平均精度均值分别提升了0.4个百分点、1.6个百分点和1.2个百分点,参数量和模型大小大幅减少,单张图像检测时间缩短。建立的玉米病虫害检测系统测试结果显示,准确率达到93.4%,检测速度达1 s 25帧。表明该系统能够满足边缘计算环境下玉米病虫害原位检测的需求。
Abstract:
In order to achieve in-situ accurate detection and identification of maize pests and diseases, this study designed an intelligent detection system for maize pests and diseases based on edge computing. The system was improved based on the YOLOv8 model with specific improvement methods, including adopting the Efficient Vision Transformer (EfficientViT) as the backbone network to reduce the computational load, introducing Ghost Convolution (GhostConv) into the feature fusion network to further reduce the computational burden, introducing Spatial-Channel Convolution (SCConv) into the C2f module to enhance the feature extraction performance, and replacing the loss function with the Wise Intersection over Union (WIoU) loss function that had a dynamic non-monotonic focusing mechanism to improve the recognition accuracy of the model. At the same time, this study designed the upper and lower computer architectures of the pest and disease detection system based on edge computing and deployed this lightweight model to the Jetson Orin Nano edge computing device. The system used Pyside6 to develop a visual interface. In addition to the recognition and training functions, it also integrated an AI expert library based on large-model technology, which could realize intelligent diagnosis of pests and diseases. The improved model YOLOv8-EGCW was tested using a self-built maize pest and disease dataset. The results showed that compared with the original YOLOv8m model, the precision, recall rate, and mean average precision of the improved model YOLOv8-EGCW increased by 0.4 percentage points, 1.6 percentage points, and 1.2 percentage points, respectively. The number of parameters and the model size were greatly reduced, and the detection time for a single image was shortened. The test results of the established corn pest and disease detection system indicated that the accuracy rate reached 93.4% and the detection speed reached 25 frames per second. These results indicated that the system could meet the requirements of in-situ detection of maize pests and diseases in the edge computing environment.

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备注/Memo

备注/Memo:
收稿日期：2024-10-30基金项目：云南省重大科技专项(202302AE090020)；云南省农业基础研究联合专项(202401BD070001-069)；云南省作物生产与智慧农业重点实验室开放课题作者简介：施杰(1981-),男,云南昆明人,博士,副教授,硕士研究生导师,主要从事智慧农业、智能农业装备研究。(E-mail)km_shijie@126.com通讯作者：杨琳琳,(E-mail)29545343@qq.com
更新日期/Last Update: 2025-03-27