YOLO-PTHD: A UAV-Based Deep Learning Model for Detecting Visible Phenotypic Signs of Pine Decline Induced by the Invasive Woodwasp Sirex noctilio (Hymenoptera, Siricidae)
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| Gepubliceerd in: | Insects vol. 16, no. 8 (2025), p. 829-853 |
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| Andere auteurs: | , , , , , , |
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| Online toegang: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3244041250 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2075-4450 | ||
| 024 | 7 | |a 10.3390/insects16080829 |2 doi | |
| 035 | |a 3244041250 | ||
| 045 | 2 | |b d20250801 |b d20250831 | |
| 084 | |a 231475 |2 nlm | ||
| 100 | 1 | |a Yang Wenshuo |u Beijing Key Laboratory for Forest Pest Control, Beijing Forestry University, Beijing 100083, China | |
| 245 | 1 | |a YOLO-PTHD: A UAV-Based Deep Learning Model for Detecting Visible Phenotypic Signs of Pine Decline Induced by the Invasive Woodwasp <i>Sirex noctilio</i> (Hymenoptera, Siricidae) | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 651 | 4 | |a China | |
| 651 | 4 | |a Heilongjiang China | |
| 653 | |a Forest management | ||
| 653 | |a Invasive species | ||
| 653 | |a Invasive insects | ||
| 653 | |a Photography | ||
| 653 | |a Signs and symptoms | ||
| 653 | |a Damage detection | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Field investigations | ||
| 653 | |a Insects | ||
| 653 | |a Performance evaluation | ||
| 653 | |a Forests | ||
| 653 | |a Datasets | ||
| 653 | |a Evergreen trees | ||
| 653 | |a Pine needles | ||
| 653 | |a Field tests | ||
| 653 | |a Remote sensing | ||
| 653 | |a Aerial surveys | ||
| 653 | |a Classification | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Trees | ||
| 653 | |a Algorithms | ||
| 653 | |a Wilt | ||
| 653 | |a Pine | ||
| 653 | |a Accuracy | ||
| 653 | |a Visual perception | ||
| 653 | |a Deep learning | ||
| 653 | |a Discoloration | ||
| 653 | |a Disease | ||
| 653 | |a Medical imaging | ||
| 653 | |a Color | ||
| 653 | |a Visual stimuli | ||
| 653 | |a Occlusion | ||
| 653 | |a Machine learning | ||
| 653 | |a Pine trees | ||
| 653 | |a Pests | ||
| 653 | |a Epidemics | ||
| 653 | |a Support vector machines | ||
| 653 | |a Data collection | ||
| 653 | |a Pest outbreaks | ||
| 653 | |a Decline | ||
| 653 | |a Morphology | ||
| 653 | |a Elongated structure | ||
| 653 | |a Sirex | ||
| 653 | |a Sirex noctilio | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Zhao Jiaqiang |u Shijiazhuang Institute of Fruit Trees, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang 050061, China | |
| 700 | 1 | |a Zhu Dexu |u Beijing Key Laboratory for Forest Pest Control, Beijing Forestry University, Beijing 100083, China | |
| 700 | 1 | |a Wang Zhengtong |u Beijing Key Laboratory for Forest Pest Control, Beijing Forestry University, Beijing 100083, China | |
| 700 | 1 | |a Song, Min |u Heilongjiang Provincial Station for Forest Pest and Disease Control and Quarantine, Harbin 140080, China | |
| 700 | 1 | |a Chen, Tao |u Fujin City Forest Pest and Disease Control and Quarantine Station, Jiamusi 146100, China | |
| 700 | 1 | |a Liang, Te |u Beijing Key Laboratory for Forest Pest Control, Beijing Forestry University, Beijing 100083, China | |
| 700 | 1 | |a Shi, Juan |u Beijing Key Laboratory for Forest Pest Control, Beijing Forestry University, Beijing 100083, China | |
| 773 | 0 | |t Insects |g vol. 16, no. 8 (2025), p. 829-853 | |
| 786 | 0 | |d ProQuest |t Agriculture Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3244041250/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3244041250/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3244041250/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |