A spatio-temporal multi-scale fusion algorithm for pine wood nematode disease tree detection

Abstract

Pine wood nematode infection is a devastating disease. Unmanned aerial vehicle (UAV) remote sensing enables timely and precise monitoring. However, UAV aerial images are challenged by small target size and complex surface backgrounds which hinder their effectiveness in monitoring. To address these challenges, based on the analysis and optimization of UAV remote sensing images, this study developed a spatio-temporal multi-scale fusion algorithm for disease detection. The multi-head, self-attention mechanism is incorporated to address the issue of excessive features generated by complex surface backgrounds in UAV images. This enables adaptive feature control to suppress redundant information and boost the model’s feature extraction capabilities. The SPD-Conv module was introduced to address the problem of loss of small target feature information during feature extraction, enhancing the preservation of key features. Additionally, the gather-and-distribute mechanism was implemented to augment the model’s multi-scale feature fusion capacity, preventing the loss of local details during fusion and enriching small target feature information. This study established a dataset of pine wood nematode disease in the Huangshan area using DJI (DJ-Innovations) UAVs. The results show that the accuracy of the proposed model with spatio-temporal multi-scale fusion reached 78.5%, 6.6% higher than that of the benchmark model. Building upon the timeliness and flexibility of UAV remote sensing, the proposed model effectively addressed the challenges of detecting small and medium-size targets in complex backgrounds, thereby enhancing the detection efficiency for pine wood nematode disease. This facilitates early preemptive preservation of diseased trees, augments the overall monitoring proficiency of pine wood nematode diseases, and supplies technical aid for proficient monitoring.

Keywords

Pine wood nematode disease / UAV remote sensing / Object detection / Deep learning / YOLOv8