Aerial Image Segmentation of Nematode-Affected Pine Trees with U-Net Convolutional Neural Network

Abstract

Pine wood nematode disease, commonly referred to as pine wilt, poses a grave threat to forest health, leading to profound ecological and economic impacts. Originating from the pine wood nematode, this disease not only causes the demise of pine trees but also casts a long shadow over the entire forest ecosystem. The accurate identification of infected trees stands as a pivotal initial step in developing effective prevention and control measures for pine wilt. Nevertheless, existing identification methods face challenges in precisely determining the disease status of individual pine trees, impeding early detection and efficient intervention. In this study, we leverage the capabilities of unmanned aerial vehicle (UAV) remote sensing technology and integrate the VGG classical small convolutional kernel network with U-Net to detect diseased pine trees. This cutting-edge approach captures the spatial and characteristic intricacies of infected trees, converting them into high-dimensional features through multiple convolutions within the VGG network. This method significantly reduces the parameter count while enhancing the sensing range. The results obtained from our validation set are remarkably promising, achieving a Mean Intersection over Union (MIoU) of 81.62%, a Mean Pixel Accuracy (MPA) of 85.13%, an Accuracy of 99.13%, and an F1 Score of 88.50%. These figures surpass those obtained using other methods such as ResNet50 and DeepLab v3+. The methodology presented in this research facilitates rapid and accurate monitoring of pine trees infected with nematodes, offering invaluable technical assistance in the prevention and management of pine wilt disease.

Keywords

• UAV remote sensing
• pine wood nematode
• machine learning
• U-Net