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Article

Detection and Location of Dead Trees with Pine Wilt Disease Based on Deep Learning and UAV Remote Sensing

by 1,2, 1,2, 1,2,* and 1,2
1
College of Engineering, South China Agricultural University, Wushan Road, Guangzhou 510642, China
2
National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Wushan Road, Guangzhou 510642, China
*
Author to whom correspondence should be addressed.
Received: 22 April 2020 / Revised: 13 May 2020 / Accepted: 15 May 2020 / Published: 21 May 2020
(This article belongs to the Special Issue Precision Agriculture Technologies for Management of Plant Diseases)
Pine wilt disease causes huge economic losses to pine wood forestry because of its destructiveness and rapid spread. This paper proposes a detection and location method of pine wood nematode disease at a large scale adopting UAV (Unmanned Aerial Vehicle) remote sensing and artificial intelligence technology. The UAV remote sensing images were enhanced by computer vision tools. A Faster-RCNN (Faster Region Convolutional Neural Networks) deep learning framework based on a RPN (Region Proposal Network) network and the ResNet residual neural network were used to train the pine wilt diseased dead tree detection model. The loss function and the anchors in the RPN of the convolutional neural network were optimized. Finally, the location of pine wood nematode dead tree was conducted, which generated the geographic information on the detection results. The results show that ResNet101 performed better than VGG16 (Visual Geometry Group 16) convolutional neural network. The detection accuracy was improved and reached to about 90% after a series of optimizations to the network, meaning that the optimization methods proposed in this paper are feasible to pine wood nematode dead tree detection. View Full-Text
Keywords: pine wilt disease; Faster-RCNN; UAV remote sensing; deep learning; geographical information pine wilt disease; Faster-RCNN; UAV remote sensing; deep learning; geographical information
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MDPI and ACS Style

Deng, X.; Tong, Z.; Lan, Y.; Huang, Z. Detection and Location of Dead Trees with Pine Wilt Disease Based on Deep Learning and UAV Remote Sensing. AgriEngineering 2020, 2, 294-307. https://0-doi-org.brum.beds.ac.uk/10.3390/agriengineering2020019

AMA Style

Deng X, Tong Z, Lan Y, Huang Z. Detection and Location of Dead Trees with Pine Wilt Disease Based on Deep Learning and UAV Remote Sensing. AgriEngineering. 2020; 2(2):294-307. https://0-doi-org.brum.beds.ac.uk/10.3390/agriengineering2020019

Chicago/Turabian Style

Deng, Xiaoling, Zejing Tong, Yubin Lan, and Zixiao Huang. 2020. "Detection and Location of Dead Trees with Pine Wilt Disease Based on Deep Learning and UAV Remote Sensing" AgriEngineering 2, no. 2: 294-307. https://0-doi-org.brum.beds.ac.uk/10.3390/agriengineering2020019

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