BiFusionNet: A lightweight model for detecting Red Turpentine Beetle infestation in pine trees

The Red Turpentine Beetle (RTB) poses a substantial threat to global pine resources. While remote sensing technologies like unmanned aerial vehicles (UAVs) offer an alternative to inefficient manual inspections, their practical application is often constrained by high computational demands and the scattered distribution of infected trees. To address these challenges, this study introduces BiFusionNet, a novel lightweight deep learning framework for RTB detection. BiFusionNet integrates several architectural innovations to achieve a superior balance between accuracy and efficiency, including a dual-path fusion backbone (DPF-Backbone) for adaptive feature extraction, a LightScale feature pyramid network (LSFPN) for efficient multi-scale fusion, and a lightweight detection head (LWDHead) to minimize computational overhead. The model’s localization accuracy is further enhanced by the Wise-CIoU loss function. On the Pests and Diseases Tree (PDT) dataset, BiFusionNet-n achieves a mean Average Precision (mAP) of 91.53 ± 0.21% at IoU 0.5. Critically, its model size of 0.8 MB and computational load of 0.9 GFLOPs are 84.6% and 85.7% lower, respectively, than those of the You Only Look Once Version 11 (YOLOv11) baseline. By effectively balancing detection accuracy with computational efficiency, BiFusionNet demonstrates significant potential as a practical solution for large-scale, UAV-based forest pest monitoring.