基于深度学习的农场虫情检测算法研究及实现  

Research and Implementation of Farm Insect Detection Algorithm Based on Deep Learning

作　　者：罗小娟[1] 胡鹏昊 LUO Xiaojuan;HU Penghao(School of Information Science and Engineering,East China University of Science and Technology,Shanghai 200237,China)

机构地区：[1]华东理工大学信息科学与工程学院,上海200237

出　　处：《华东理工大学学报（自然科学版）》2024年第5期732-739,共8页Journal of East China University of Science and Technology

摘　　要：传统的病虫害防治手段需要消耗大量人力、物力,且达不到很高的精确度,为了更科学、高效地做好农场病虫害防治工作,本文结合深度学习技术和物联网技术研发了虫情检测系统进行病虫害的远程检测,提高防治工作的效率。该系统采用YOLO-v5网络模型,结合迁移学习,训练学习了林业常见害虫和农田常见害虫的特征,实现了高效的检测识别。基于物联网技术实现远程控制拍摄病虫害图像,并通过Wi-Fi传输到计算机端进行识别,通过可视化界面呈现出农田中虫害的种类和数量,对减少人力、物力消耗以及实现科学防虫具有良好的实际应用价值。Traditional pest control methods require a lot of manpower and material resources,and cannot achieve high accuracy.In order to do a more scientific and efficient pest control work,this paper combined deep learning technology and internet of things technology to develop a pest detection system for remote pest detection and improve the efficiency of control work.The system mainly adopted the YOLO-v5 network model combined with transfer learning to train and learn the characteristics of common forest pests and farmland pests,so as to achieve efficient detection and identification.Based on the internet of things technology,remote control was achieved to capture images of pests and diseases,and they were transmitted to the computer for recognition through Wi-Fi.The types and quantities of pests in the field were shown through the visual interface.It has good practical value for reducing the consumption of manpower and material resources and realizing the scientific pest control.

关 键 词：农场虫情检测 深度学习 物联网技术 YOLO-v5网络模型 图像处理识别 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]