{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T04:26:14Z","timestamp":1787199974376,"version":"build-2736575974"},"reference-count":65,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T00:00:00Z","timestamp":1786924800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Yuelushan Laboratory Breeding Program","award":["Grant 1132 No. YLS-2025-ZY02048"],"award-info":[{"award-number":["Grant 1132 No. YLS-2025-ZY02048"]}]},{"name":"the Top Ten Technical Research Projects in Hunan Province","award":["2024NK1020"],"award-info":[{"award-number":["2024NK1020"]}]},{"name":"the Special Project for the Construction"},{"name":"Operation of the State Key Laboratory of Woody Oil Resources Utilization under the Ministry of Science and Technology of China."}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Plants"],"abstract":"<jats:p>Camellia oleifera leaf disease segmentation under natural field conditions is important for precision plant protection but remains challenging because lesions often show small target areas, blurred boundaries, uneven illumination, complex backgrounds, and coexisting symptoms. To address these problems, this paper proposes BFMambaNet, a Boundary-Frequency-guided Global Semantic Mamba Network for fine-grained disease segmentation. The model adopts an encoder-decoder framework and introduces a Global Semantic Mamba-based spatial selective feature modeling block to capture long-range lesion context and reduce semantic confusion. A gated wavelet spatial enhancement block is further designed to strengthen high-frequency boundary details while suppressing noisy responses. During training, boundary-frequency auxiliary supervision guides contour localization and pathological texture recovery without additional manual boundary labels. A reinforcement-learning-guided adaptive loss controller adjusts class-wise reweighting factors and loss-component weights according to the training state, improving optimization stability. A pixel-level dataset containing 1400 images and seven disease categories was constructed for evaluation. Experimental results show that BFMambaNet achieves 92.39% Precision, 91.43% Recall, 91.26% Dice, and 85.46% mIoU, outperforming representative CNN-based, Transformer-based, and Mamba-based models. Evaluations on environmental subsets confirm superior robustness, outperforming VMamba by 3.70% mIoU under uneven illumination, 3.55% mIoU under complex backgrounds, and 5.10% mIoU under coexisting symptoms. Cross-dataset validation on Apple leaf diseases further proves its generalization with 3.39% mIoU and 3.84% Dice improvements over U-Mamba, while maintaining a competitive inference speed of 30 FPS. Qualitative results also show clearer boundaries, fewer missed small lesions, and more stable predictions in complex field scenarios.<\/jats:p>","DOI":"10.3390\/plants15162493","type":"journal-article","created":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T13:42:17Z","timestamp":1786974137000},"page":"2493","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["BFMambaNet: Boundary-Frequency-Guided Global Semantic Mamba Network for Fine-Grained Camellia oleifera Leaf Disease Segmentation"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-9983-3356","authenticated-orcid":false,"given":"Xuanhao","family":"Li","sequence":"first","affiliation":[{"name":"School of Electronic Information and Physics, Central South University of Forestry and Technology, Changsha 410004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fulin","family":"Su","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Physics, Central South University of Forestry and Technology, Changsha 410004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongming","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Physics, Central South University of Forestry and Technology, Changsha 410004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaofeng","family":"Peng","sequence":"additional","affiliation":[{"name":"Yuelushan Laboratory, Changsha 410128, China"},{"name":"Hunan Academy of Forestry, Changsha 410004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Physics, Central South University of Forestry and Technology, Changsha 410004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fangying","family":"Wan","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Physics, Central South University of Forestry and Technology, Changsha 410004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruifeng","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Forestry, Central South University of Forestry and Technology, Changsha 410004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Quan, W., Wang, A., Gao, C., and Li, C. 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