DOI number:10.3390/rs15102671

Journal:REMOTE SENSING

Key Words:pine wilt disease (PWD),YOLOv5-PWD,deep learning,remote sensing,single-tree level detection,object detection,Sentinel-2

Abstract:In recent years, remote sensing techniques such as satellite and drone-based imaging have been used to monitor Pine Wilt Disease (PWD), a widespread forest disease that causes the death of pine species. Researchers have explored the use of remote sensing imagery and deep learning algorithms to improve the accuracy of PWD detection at the single-tree level. This study introduces a novel framework for PWD detection that combines high-resolution RGB drone imagery with free-access Sentinel-2 satellite multi-spectral imagery. The proposed approach includes an PWD-infected tree detection model named YOLOv5-PWD and an effective data augmentation method. To evaluate the proposed framework, we collected data and created a dataset in Xianning City, China, consisting of object detection samples of infected trees at middle and late stages of PWD. Experimental results indicate that the YOLOv5-PWD detection model achieved 1.2% higher mAP compared to the original YOLOv5 model and a further improvement of 1.9% mAP was observed after applying our dataset augmentation method, which demonstrates the effectiveness and potential of the proposed framework for PWD detection.

Co-author:Haobo Yang,Xianwei Li,Kun Zhu,Tong Wang,Puyun Liao,Mengdi Han,Yuanfu Gong,Qing Wang

Indexed by:Journal paper

Correspondence Author:Xiaodong Zhang

Document Type:J

Volume:15

Issue:10

ISSN No.:2072-4292

Translation or Not:no

Date of Publication:2023-05-20

Included Journals:SCI