{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T00:33:20Z","timestamp":1793147600246,"version":"4.5.0"},"reference-count":47,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T00:00:00Z","timestamp":1768262400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Plant Sci."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Pine wilt disease (PWD) is a highly destructive infectious disease that severely damages pine forests worldwide. Because symptoms emerge first in the tree crown, detection from unmanned aerial vehicles (UAVs) is efficient. However, most methods perform only binary classification and lack pixel-level staging, which leads to missed initial symptoms and confusion with other species.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We propose MSCF-LUNet, a lightweight three-stage semantic segmentation model based on multi-scale context fusion. The model uses an improved multi-scale patch embedding guided by attention with relative position encoding (AWRP) to adapt the sampling field of view and to fuse local details with global context. Under contextual attention, the network learns fine-grained features and location cues.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In complex environments, MSCF-LUNet achieves 89.56% precision, 92.13% recall, 88.92% intersection over union (IoU), and 96.54% pixel accuracy (PA), balancing performance and computational cost.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The model effectively segments PWD-infected regions and determines disease stages from remote-sensing imagery.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpls.2025.1727626","type":"journal-article","created":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T06:31:28Z","timestamp":1768285888000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["MSCF-LUNet: a lightweight three-stage pine wilt disease segmentation model with multi-scale context fusion mechanism"],"prefix":"10.3389","volume":"16","author":[{"given":"Dejing","family":"Zhou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junxian","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenxi","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tiantian","family":"Meng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanhang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baihan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengting","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yubin","family":"Lan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianyi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,1,13]]},"reference":[{"key":"B1","first-page":"683","article-title":"A Novel Focal Tversky loss function with Improved Attention U-Net for lesion segmentation","author":"Abraham","year":"2019"},{"key":"B2","first-page":"27706","article-title":"Poly kernel inception network for remote sensing detection","volume-title":"IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Cai","year":"2024"},{"key":"B3","first-page":"205","article-title":"Swin-unet: Unet-like pure transformer for medical image segmentation","volume-title":"European Conference on Computer Vision","author":"Cao","year":"2021"},{"key":"B4","unstructured":"Chen\n              J.\n            \n            \n              Lu\n              Y.\n            \n            \n              Yu\n              Q.\n            \n            \n              Luo\n              X.\n            \n            \n              Adeli\n              E.\n            \n            \n              Wang\n              Y.\n            \n          \n          arXiv\n          TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation\n          \n          2021"},{"key":"B5","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-01234-2_49","article-title":"Encoder-decoder with atrous separable convolution for semantic image segmentation","author":"Chen","year":"2018"},{"key":"B6","doi-asserted-by":"publisher","first-page":"5962","DOI":"10.1109\/TPAMI.2021.3087709","article-title":"ArcFace: additive angular margin loss for deep face recognition","volume":"44","author":"Deng","year":"2022","journal-title":"IEEE Trans. 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