Surveillance of pine wilt disease by high resolution satellite

Abstract

Pine wilt disease caused by the pinewood nematode Bursaphelenchus xylophilus has led to the death of a large number of pine trees in China. This destructive disease has the characteristics of bring wide-spread, fast onset, and long incubation time. Most importantly, in China, the fatality rate in pines is as high as 100%. The key to reducing this mortality is how to quickly find the infected trees. We proposed a method of automatically identifying infected trees by a convolution neural network and bounding box tool. This method rapidly locates the infected area by classifying and recognizing remote sensing images obtained by high resolution earth observation Satellite. The recognition accuracy of the test data set was 99.4%, and the remote sensing image combined with convolution neural network algorithm can identify and determine the distribution of the infected trees. It can provide strong technical support for the prevention and control of pine wilt disease.