{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T06:14:55Z","timestamp":1788243295259,"version":"build-2803163510"},"reference-count":27,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"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:p>Precise segmentation of small and dense field crop pests in UAV images remains challenging due to limited long-range dependency modeling in CNNs and quadratic complexity in ViTs. Although VM-UNet captures global context efficiently, it lacks local texture detail and multi-scale feature integration for small targets. To address these limitations, this paper proposes CFAEVM-UNet (CrossFusion Attention Enhanced VM-UNet), which incorporates three core modules: a Visual State Space (VSS) block for efficient long-range dependency modeling, a Local-Global Spatial-Channel Gated Attention (LG-SCGA) module for multi-scale gated attention fusion, and a Multi-Stage Channel-Wise Attention (MSCWA) module for cross-stage channel reweighting. Extensive experiments on an integrated dataset (IP102 subset combined with UAV-captured field images) demonstrate that CFAEVM-UNet achieves state-of-the-art performance, attaining an mIoU of 81.23% and a DSC of 83.67%, outperforming U-Net by 8.89% in mIoU. This work provides an effective and practical solution for automated pest monitoring in precision agriculture.<\/jats:p>","DOI":"10.3389\/fpls.2026.1896392","type":"journal-article","created":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T05:33:18Z","timestamp":1788240798000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["CFAEVM-UNet: cross-fusion attention enhanced VM-UNet for UAV image segmentation of small and dense field crop pests"],"prefix":"10.3389","volume":"17","author":[{"given":"Guohong","family":"Qi","sequence":"first","affiliation":[{"name":"Faculty of Engineering, Zhengzhou SIAS University, XinZheng","place":["Henan, China"]},{"name":"Henan Engineering Research Center for Agricultural Information Digital & Intelligent Technology","place":["Zhengzhou, Henan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Zhengzhou SIAS University, XinZheng","place":["Henan, China"]},{"name":"Henan Engineering Research Center for Agricultural Information Digital & Intelligent Technology","place":["Zhengzhou, Henan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanwen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Henan Engineering Research Center for Agricultural Information Digital & Intelligent Technology","place":["Zhengzhou, Henan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,1]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"17581","DOI":"10.1109\/JSEN.2021.3071290","article-title":"Agrisegnet: Deep aerial semantic segmentation framework for IoT-assisted precision agriculture","volume":"21","author":"Anand","year":"2021","journal-title":"IEEE Sens. 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