Unraveling spatiotemporal dynamics of pine wilt disease via time-series UAV monitoring and deep learning.

Abstract

Background: This study investigates the spatiotemporal dynamics of pine wilt disease (PWD) to inform data-driven management. We implemented a time-series monitoring framework in a township in Zhejiang Province, China, acquiring seven sequences of unmanned aerial vehicle (UAV) orthomosaics between 2022 and 2024. An enhanced YOLOX-based change detection model was developed and trained on 300 000 samples. This model exploits phenological variations to automatically identify PWD-discolored pines, effectively filtering confounding objects.

Results: The model demonstrated robust performance, achieving an Average Precision (AP) of 0.89, with Precision and Recall exceeding 85%. Analysis revealed a consistent westward expansion and a progressive increase in disease hotspots. Crucially, winter surveys detected substantial delayed-symptom pines missed in autumn, roughly equivalent to the autumn baseline. Consequently, the annual cumulative mortality caused by PWD was nearly double (2×) the autumn count. Over 90% of trees newly identified in autumn were located within 300 m of infections detected the previous spring, indicating strong spatial clustering. Furthermore, 80% of infected trees occurred at elevations < 400 m and slopes < 29°, aligning with prevailing easterly winds.

Conclusion: This research establishes a validated framework bridging remote sensing and on-the-ground sanitation. By quantifying the symptom lag effect (which doubles mortality estimates relative to traditional autumn surveys) and elucidating environmentally driven spread mechanisms, we provide a scientific basis for correcting census biases and optimizing resource allocation for precise PWD management. © 2026 Society of Chemical Industry.