The pine wilt disease (PWD) is one of the most dangerous and destructive diseases to coniferous forests. The rapid spread trend and strong destruction directly threaten the security of forests. The complex spread pattern and the hard labor process of diagnosis call for an effective way to detect the infected areas. In this paper, an airborne edge-computing and lightweight deep learning based system are designed for PWD detection by using imagery sensors. Unmanned aerial vehicle (UAV) is firstly utilized to realize a large-scale coverage of forests, which can substantially reduce the hard labor. Except for infected trees, a large number of irrelevant images are also acquired by the UAV, which will overload the burden of process and transmission. Then a lightweight improved YOLOv4-Tiny based method (named as YOLOv4-Tiny-3Layers) is proposed to filter these uninterested images by leveraging the computation capability of edge computing, which can realize a fast coarse-grained detection with a low missing rate. Finally, all the remaining images are transmitted to the ground workstation for the final fine-grained detection. Experimental results show that the proposed system can implement a fast detection with superior performance as compared to other methods, which helps to detect the infected pine trees in a quick manner.
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