Intelligent Identification of Pine Wilt Disease Infected Individual Trees Using UAV-Based Hyperspectral Imagery

Abstract

The pine wood nematode (PWN; Bursaphelenchus xylophilus) is a major invasive species in China, causing huge economic and ecological damage to the country due to the absence of natural enemies and the extremely rapid rate of infection and spread. Accurate monitoring of pine wilt disease (PWD) is a prerequisite for timely and effective disaster prevention and control. UAVs can carry hyperspectral sensors for near-ground remote sensing observations, which can obtain rich spatial and spectral information and have the potential for infected tree identification. Deep learning techniques can use rich multidimensional data to mine deep features in order to achieve tasks such as classification and target identification. Therefore, we propose an improved Mask R-CNN instance segmentation method and an integrated approach combining a prototypical network classification model with an individual tree segmentation algorithm to verify the possibility of deep learning models and UAV hyperspectral imagery for identifying infected individual trees at different stages of PWD. The results showed that both methods achieved good performance for PWD identification: the overall accuracy of the improved Mask R-CNN with the screened bands as input data was 71%, and the integrated method combining prototypical network classification model with individual tree segmentation obtained an overall accuracy of 83.51% based on the screened bands data, in which the early infected pine trees were identified with an accuracy of 74.89%. This study indicates that the improved Mask R-CNN and integrated prototypical network method are effective and practical for PWD-infected individual trees identification using UAV hyperspectral data, and the proposed integrated prototypical network enables early identification of PWD, providing a new technical guidance for early monitoring and control of PWD.