文献类型： 外文期刊

作者： Yang, Wenshuo ¹ ; Zhao, Jiaqiang ³ ; Zhu, Dexu ¹ ; Wang, Zhengtong ¹ ; Song, Min ⁴ ; Chen, Tao ⁵ ; Liang, Te ¹ ; Shi, Juan ¹ ;

作者机构： 1.Beijing Forestry Univ, Beijing Key Lab Forest Pest Control, Beijing 100083, Peoples R China

2.Beijing Forestry Univ, Sino French Joint Lab Invas Forest Pests Eurasia, Beijing 100083, Peoples R China

3.Hebei Acad Agr & Forestry Sci, Shijiazhuang Inst Fruit Trees, Shijiazhuang 050061, Peoples R China

4.Heilongjiang Prov Stn Forest Pest & Dis Control &, Harbin 140080, Peoples R China

5.Fujin City Forest Pest & Dis Control & Quarantine, Jiamusi 146100, Peoples R China

关键词： invasive species; Sirex noctilio; deep learning; YOLO; UAV image; pine tree; object detection

期刊名称：INSECTS （ 影响因子：2.9； 五年影响因子：3.3 ）

ISSN：

年卷期： 2025 年 16 卷 8 期

页码：

收录情况： SCI

摘要： Sirex noctilio is an invasive pest that contributes to pine tree decline, leading to visual symptoms such as needle discoloration, crown thinning, and eventual tree death. Detecting these visible phenotypic signs from drone imagery is challenging due to elongated or irregular crown shapes, weak color differences, and occlusion within dense forests. This study introduces YOLO-PTHD, a lightweight deep learning model designed for detecting visible signs of pine decline in UAV images. The model integrates three customized components: Strip-based convolution to capture elongated tree structures, Channel-Aware Attention to enhance weak visual cues, and a scale-sensitive dynamic loss function to improve detection of minority classes and small targets. A UAV-based dataset, the Sirex Woodwasp dataset, was constructed with annotated images of weakened, and dead pine trees. YOLO-PTHD achieved an mAP of 0.923 and an F1-score of 0.866 on this dataset. To evaluate the model's generalization capability, it was further tested on the Real Pine Wilt Disease dataset from South Korea. Despite differences in tree symptoms and imaging conditions, the model maintained strong performance, demonstrating its robustness across different forest health scenarios. Field investigations targeting Sirex woodwasp in outbreak areas confirmed that the model could reliably detect damaged trees in real-world forest environments. This work demonstrates the potential of UAV-based visual analysis for large-scale phenotypic surveillance of pine health in forest management.