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Using an improved SIFT-RANSAC-based registration method, high-precision alignment between RGB and near-infrared (840\u00a0nm) bands is achieved, resulting in spatially consistent multi-channel inputs. Second,\u00a0to meet the real-time requirements of edge deployment, the model architecture integrates the hierarchical semantic design of HGNetv2 and the efficiency of Ghost convolution, significantly reducing the parameter count while maintaining a strong capacity for extracting multi-scale PWD features. Finally, a detection architecture incorporating a Lightweight Shared Convolutional Detection (LSCD) head and a Multi-Path Coordinate Attention (MPCA) module is designed. Through parameter sharing and an adaptive feature enhancement mechanism, the detection accuracy for weakly visible targets is further improved. The model contains only 3.8\u00a0M parameters and 9.5 GFLOPs, achieving a precision of 0.88, recall of 0.818, F1-score of 0.9, and mAP of 0.848 in testing, outperforming mainstream models and\u00a0striking a notable balance between accuracy and efficiency. Ablation studies show that introducing the near-infrared band improves key metrics including precision, recall, F1-score, mAP by 4.0%, 3.5%, 2.8%, and 3.8%, respectively, validating the effectiveness of multi-spectral fusion for early PWD detection. This study provides a feasible technical solution for\u00a0resource-constrained UAV platforms\u00a0to achieve early, accurate, and real-time monitoring of Pine Wilt Disease. The code is available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/123456WPF\/PWD-MFS\/tree\/master\" ext-link-type=\"uri\">https:\/\/github.com\/123456WPF\/PWD-MFS\/tree\/master<\/jats:ext-link>\n                  <\/jats:p>","DOI":"10.1007\/s44443-026-00519-7","type":"journal-article","created":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T12:43:08Z","timestamp":1771332188000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["PWD-MFS: a lightweight multispectral fusion network for detection of pine wilt disease with UAV imagery"],"prefix":"10.1007","volume":"38","author":[{"given":"Pengfei","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baohua","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenya","family":"Tao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Longwa","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Songyan","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziwei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,17]]},"reference":[{"key":"519_CR1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2024.3505964","author":"J Anandakrishnan","year":"2024","unstructured":"Anandakrishnan J, Sangaiah AK, Darmawan H (2024a) Precise spatial prediction of rice seedlings from large scale airborne remote sensing data using optimized Li-YOLOv9. 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