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The key enhancements include the integration of the Convolutional Block Attention Module (CBAM) for refined feature representation, the adoption of the Normalized Wasserstein Distance (NWD) loss for robust small-object localization, the incorporation of Deformable Convolution v3 (DCNv3) to adapt to irregular shapes, and the replacement of standard convolutions with GhostConv for a lightweight design. Experiments on a self-built urban tree dataset show that YOLO-CNGD achieves a precision of 94.8%, a recall of 91.1%, and an mAP@0.5 of 93.7%. The model balances accuracy and efficiency, showing great potential for large-scale automated urban tree inventory.<\/jats:p>","DOI":"10.3389\/fpls.2026.1754458","type":"journal-article","created":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T07:05:39Z","timestamp":1772003139000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Research on urban tree classification method based on YOLO-CNGD"],"prefix":"10.3389","volume":"17","author":[{"given":"Cunjin","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinglong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhixin","family":"Gu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,2,25]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1080\/07038992.2017.1252907","article-title":"Layer stacking: A novel algorithm for individual forest tree segmentation from LiDAR point clouds","volume":"43","author":"Ayrey","year":"2017","journal-title":"Can. 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