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UAV-based detection methods can significantly improve monitoring efficiency; however, small target scales, blurred details, and complex backgrounds in UAV imagery pose great challenges to detection algorithms. To address these issues, This paper proposes TLK-YOLO, a UAV-based method for detecting pine trees infested by the Red Turpentine Beetle, which integrates a Transformer mechanism with a large-kernel selection strategy. A Local Window Cross-Attention (LWCA) upsampling module is introduced to enhance small-object detail modeling by fusing high- and low-resolution information within local windows. A Dynamic Combined Large Selective Kernel (DCLSK) module adaptively adjusts the receptive field to balance global context and local details, improving small-object feature discriminability and robustness. Furthermore, a redesigned network architecture, TLK-NA, mitigates redundancy in shallow-to-deep feature propagation by reversing the information flow from deep to shallow layers, achieving a better trade-off between detection accuracy and computational efficiency. Experimental results demonstrate that TLK-YOLO exhibits remarkable performance in detecting Red Turpentine Beetle infestations from UAV imagery, achieving an mAP50 of 89.3%. Compared with the baseline model, TLK-YOLO attains comprehensive improvements in both detection precision and recall, with precision increases by 4.4%, recall precision by 9.6%, while mAP50 and mAP50-95 improve by 7.9% and 8.3%, respectively, while maintaining relatively low computational overhead. This network provides an efficient and reliable technical foundation for UAV-based forestry pest and disease monitoring.<\/jats:p>","DOI":"10.1038\/s41598-026-59736-9","type":"journal-article","created":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T04:19:12Z","timestamp":1782879552000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A transformer and large-kernel convolution-based detection model for Red Turpentine Beetle infestation in pine trees"],"prefix":"10.1038","volume":"16","author":[{"given":"Manxin","family":"Chao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Can","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junfeng","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huihua","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zaiqing","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijun","family":"Yun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuesong","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,1]]},"reference":[{"key":"59736_CR1","doi-asserted-by":"publisher","first-page":"445","DOI":"10.3390\/agronomy15020445","volume":"15","author":"J Wang","year":"2025","unstructured":"Wang, J. et al. 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