%0 Journal Article
%T 基于无人机航拍图像的YOLOv5松材线虫病监测研究<br>The Study of Pine Wood Nematode Disease Detection Using YOLOv5 Based on Unmanned Aerial Vehicle Aerial Images
%A 杨蒙达
%J Artificial Intelligence and Robotics Research
%P 1-8
%@ 2326-3423
%D 2024
%I Hans Publishing
%R 10.12677/AIRR.2024.131001
%X 松材线虫病是一种严重危害林木的疾病，由微小的松材线虫引发，主要寄生于松树和其他针叶树种。这种疾病通过线虫的侵入和繁殖，释放毒素导致树木逐渐衰退直至死亡。它不仅对林业产业构成威胁，还对生态系统的稳定性有重大影响，并且具有高度传染性，在森林中迅速扩散。为了控制其传播，采取了包括对木材国际贸易限制在内的严格检疫和防治措施。此外，本研究采用了先进的目标检测网络技术YOLOv5进行松材线虫病的监测，经过500个epoch的训练，实现了0.88的平均精度(mAP)。本文还提出了一种用于无人机航拍的实时高效监测解决方案，以加强病害控制，为相关领域的研究和应用提供了有力支持。<br />
Pine Wood Nematode Disease is a serious affliction damaging forest trees, caused by the tiny pine wood nematode. This nematode primarily parasitizes pine trees and other conifer species, leading to the gradual decline and eventual death of infected trees. The disease not only threatens the forestry industry but also has a significant impact on the stability of ecosystems due to its highly contagious nature, spreading rapidly in forest ecosystems. To curb the spread of pine wilt disease, strict quarantine and control measures have been adopted by various countries, including restrictions on the international trade of wood and wood products to prevent the spread of the nematode. Additionally, this study utilized advanced target detection network technology, YOLOv5, for monitoring pine wilt disease. After training through 500 epochs, it achieved an average precision (mAP) of 0.88. The paper also proposes a real-time and efficient monitoring solution using drones, enhancing the monitoring and control of pine wilt disease and providing robust support for research and application in related fields.
%K 松材线虫病，YOLOv5，目标检测，深度学习，林木健康监测<br>Pine Wood Nematode Disease
%K YOLOv5
%K Object Detection
%K Deep Learning
%K Forest Tree Health 
Monitoring
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=80522