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Article

Identifying Pine Wood Nematode Disease Using UAV Images and Deep Learning Algorithms

by 1, 1,*, 1,2, 1 and 1
1
School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China
2
Anhui Engineering Research Center for Geographical Information Intelligent Technology, Hefei 230601, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2021, 13(2), 162; https://doi.org/10.3390/rs13020162
Received: 17 November 2020 / Revised: 23 December 2020 / Accepted: 4 January 2021 / Published: 6 January 2021
(This article belongs to the Special Issue Individual Tree Detection and Characterisation from UAV Data)
Pine nematode is a highly contagious disease that causes great damage to the world’s pine forest resources. Timely and accurate identification of pine nematode disease can help to control it. At present, there are few research on pine nematode disease identification, and it is difficult to accurately identify and locate nematode disease in a single pine by existing methods. This paper proposes a new network, SCANet (spatial-context-attention network), to identify pine nematode disease based on unmanned aerial vehicle (UAV) multi-spectral remote sensing images. In this method, a spatial information retention module is designed to reduce the loss of spatial information; it preserves the shallow features of pine nematode disease and expands the receptive field to enhance the extraction of deep features through a context information module. SCANet reached an overall accuracy of 79% and a precision and recall of around 0.86, and 0.91, respectively. In addition, 55 disease points among 59 known disease points were identified, which is better than other methods (DeepLab V3+, DenseNet, and HRNet). This paper presents a fast, precise, and practical method for identifying nematode disease and provides reliable technical support for the surveillance and control of pine wood nematode disease. View Full-Text
Keywords: UAV remote sensing; pine wood nematode disease; deep learning; intelligent identifying UAV remote sensing; pine wood nematode disease; deep learning; intelligent identifying
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MDPI and ACS Style

Qin, J.; Wang, B.; Wu, Y.; Lu, Q.; Zhu, H. Identifying Pine Wood Nematode Disease Using UAV Images and Deep Learning Algorithms. Remote Sens. 2021, 13, 162. https://doi.org/10.3390/rs13020162

AMA Style

Qin J, Wang B, Wu Y, Lu Q, Zhu H. Identifying Pine Wood Nematode Disease Using UAV Images and Deep Learning Algorithms. Remote Sensing. 2021; 13(2):162. https://doi.org/10.3390/rs13020162

Chicago/Turabian Style

Qin, Jun, Biao Wang, Yanlan Wu, Qi Lu, and Haochen Zhu. 2021. "Identifying Pine Wood Nematode Disease Using UAV Images and Deep Learning Algorithms" Remote Sensing 13, no. 2: 162. https://doi.org/10.3390/rs13020162

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