| Title |
Pine Wilt Disease Detection Based on Deep Learning Using an Unmanned Aerial Vehicle |
| Authors |
임언택(Lim, Eon Taek) ; 도명식(Do, Myung Sik) |
| DOI |
https://doi.org/10.12652/Ksce.2021.41.3.0317 |
| Keywords |
무인항공기; 소나무재선충병; 이미지 분할방법; 이미지 검출방법; 딥러닝 UAV; Pine wilt disease; YOLOv2; SegNet; Deep learning |
| Abstract |
Pine wilt disease first appeared in Busan in 1998; it is a serious disease that causes enormous damage to pine trees. The Korean government enacted a special law on the control of pine wilt disease in 2005, which controls and prohibits the movement of pine trees
in affected areas. However, existing forecasting and control methods have physical and economic challenges in reducing pine wilt disease that occurs simultaneously and radically in mountainous terrain. In this study, the authors present the use of a deep learning object recognition and prediction method based on visual materials using an unmanned aerial vehicle (UAV) to effectively detect trees suspected of being infected with pine wilt disease. In order to observe pine wilt disease, an orthomosaic was produced using image data acquired through aerial shots. As a result, 198 damaged trees were identified, while 84 damaged trees were identified in field surveys that excluded areas with inaccessible steep slopes and cliffs. Analysis using image segmentation (SegNet) and image detection (YOLOv2) obtained a performance value of 0.57 and 0.77, respectively. |