Abstract
Pine wilt disease (PWD), caused by the nematode Bursaphelenchus xylophilus, is a highly destructive forest disease that necessitates rapid and precise identification for effective management and control. This study evaluates various detection methods for PWD, including morphological diagnosis, molecular techniques, and remote sensing. While traditional methods are economical, they are limited by their inability to detect subtle or early changes and require considerable time and expertise. To overcome these challenges, this study emphasizes advanced molecular approaches such as real-time polymerase chain reaction (RT-PCR), droplet digital PCR (ddPCR), and loop-mediated isothermal amplification (LAMP) coupled with CRISPR/Cas12a, which offer fast and accurate pathogen detection. Additionally, DNA barcoding and microarrays facilitate species identification, and proteomics can provide insights into infection-specific protein signatures. The study also highlights remote sensing technologies, including satellite imagery and unmanned aerial vehicle (UAV)-based hyperspectral analysis, for their capability to monitor PWD by detecting asymptomatic diseases through changes in the spectral signatures of trees. Future research should focus on combining traditional and innovative techniques, refining visual inspection processes, developing rapid and portable diagnostic tools for field application, and exploring the potential of volatile organic compound analysis and machine learning algorithms for early disease detection. Integrating diverse methods and adopting innovative technologies are crucial to effectively control this lethal forest disease.