Detection of the Infection Stage of Pine Wilt Disease and Spread Distance Using Monthly UAV-Based Imagery and a Deep Learning Approach

Pine wood nematode (PWN) is an invasive species which causes pine wilt disease (PWD), posing a significant threat to coniferous forests globally. Despite its destructive nature, strategies for the management of PWD spread lack a comprehensive understanding of the occurrence pattern of PWNs. This study investigates the outbreak timing and spread distances of PWD on a monthly scale. Two regions (A and B) in southeastern China, characterized by varying mixed ratios of coniferous and broadleaf trees, were examined. Infected trees were classified into early, middle, late, and dead stages. Monthly unmanned aerial vehicle (UAV) RGB data covering one year and three deep learning algorithms (i.e., Faster R-CNN, YOLOv5, and YOLOv8) were employed to identify the stress stages and positions of the trees. Further, each month, newly infected trees were recorded to calculate spread distances from the location of surrounding trees. The results indicate that the YOLOv5 model achieved the highest accuracy (mean average precision (mAP) = 0.58, F1 = 0.63), followed by Faster R-CNN (mAP = 0.55, F1 = 0.58) and YOLOv8 (mAP = 0.57, F1 = 0.61). Two PWD outbreak periods occurred between September-October and February of the following year, with early and middle-stage outbreaks in August and September and late and dead-tree outbreaks occurring between October and February of the following year. Over one year, the nearest spread distance for PWD-infected trees averaged 12.54 m (median: 9.24 m) for region A in September and 13.14 m (median: 10.26 m) for region B in October. This study concludes that February through August represents the optimal period for PWD control. Additionally, mixed conifer-broadleaf forests with a higher proportion of broadleaf trees prove beneficial in mitigating PWD outbreaks and reducing the number of infected trees. This work demonstrates the effectiveness of integrating monthly UAV-based imagery and deep learning algorithms for monitoring PWD outbreak times and spread distances, offering technical support for forest pest prevention and management.