Early detection of pine wilt disease based on UAV reconstructed hyperspectral image

Pine wilt disease (PWD) is a highly destructive infectious disease that affects pine forests. Therefore, an accurate and effective method to monitor PWD infection is crucial. However, the majority of existing technologies can detect PWD only in the later stages. To curb the spread of PWD, it is imperative to develop an efficient method for early detection. We presented an early stage detection method for PWD utilizing UAV remote sensing, hyperspectral image reconstruction, and SVM classification. Initially, employ UAV to capture RGB remote sensing images of pine forests, followed by labeling infected plants using these images. Hyperspectral reconstruction networks, including HSCNN+, HRNet, MST++, and a self-built DW3D network, were employed to reconstruct the RGB images obtained from remote sensing. This resulted in hyperspectral images in the 400-700nm range, which were used as the dataset of early PWD detection in pine forests. Spectral reflectance curves of infected and uninfected plants were extracted. SVM algorithms with various kernel functions were then employed to detect early pine wilt disease. The results showed that using SVM for early detection of PWD infection based on reconstructed hyperspectral images achieved the highest accuracy, enabling the detection of PWD in its early stage. Among the experiments, MST++, DW3D, HRNet, and HSCNN+ were combined with Poly kernel SVM performed the best in terms of cross-validation accuracy, achieving 0.77, 0.74, 0.71, and 0.70, respectively. Regarding the reconstruction network parameters, the DW3D network had only 0.61M parameters, significantly lower than the MST++ network, which had the highest reconstruction accuracy with 1.6M parameters. The accuracy was improved by 27% compared to the detection results obtained using RGB images. This paper demonstrated that the hyperspectral reconstruction-poly SVM model could effectively detect the Early stage of PWD. In comparison to UAV hyperspectral remote sensing methods, the proposed method in this article offers a same precision, but a higher operational efficiency and cost-effectiveness. It also enables the detection of PWD at an earlier stage compared to RGB remote sensing, yielding more accurate and reliable results.