{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T10:52:14Z","timestamp":1777632734730,"version":"3.51.4"},"reference-count":31,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T00:00:00Z","timestamp":1777248000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2024YFD2000303"],"award-info":[{"award-number":["2024YFD2000303"]}]},{"name":"Science and Technology Research program of Henan Province","award":["232102110303"],"award-info":[{"award-number":["232102110303"]}]},{"name":"Key Research and Development Special Project of Henan Province","award":["261111113800"],"award-info":[{"award-number":["261111113800"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>In intensive farming, contactless individual pig identification is crucial for precision feeding and health monitoring. However, real-world barn conditions\u2014such as fluctuating illumination, severe occlusions, non-rigid poses, and high inter-individual similarity\u2014pose significant challenges. Existing models struggle to balance high accuracy with lightweight deployment. To address this, we propose YOLO-ESO, an optimized detection framework based on YOLOv10n. YOLO-ESO introduces three core innovations: (1) integrating the C2f_ODConv module into the backbone to strengthen feature learning under complex poses via dynamic convolution; (2) redesigning the neck with a Semantics and Detail Infusion (SDI) module to improve multi-scale fusion while suppressing background noise; and (3) embedding an Efficient Multi-Scale Attention (EMA) mechanism before the detection head to capture fine-grained identity cues like texture and contours. Evaluated on a real-world pig dataset, YOLO-ESO achieves an mAP@0.5 of 96.6%, an mAP@0.5:0.95 of 71.1%, and an F1 of 92.0%. YOLO-ESO surpasses state-of-the-art detectors including YOLOv8, YOLOv11, and RT-DETR, while introducing only 8.7 GFLOPs and 3.48 million parameters. Overall, the proposed YOLO-ESO provides an accurate and lightweight solution for robust individual pig identification in complex farming environments, showing strong potential for practical deployment in precision livestock farming.<\/jats:p>","DOI":"10.3390\/info17050421","type":"journal-article","created":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T11:33:37Z","timestamp":1777376017000},"page":"421","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["YOLO-ESO: A Lightweight YOLOv10-Based Model for Individual Pig Identification in Complex Farming Environments"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7291-1477","authenticated-orcid":false,"given":"Juanhua","family":"Zhu","sequence":"first","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lele","family":"Song","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tong","family":"Fu","sequence":"additional","affiliation":[{"name":"College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0392-3107","authenticated-orcid":false,"given":"Yan","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miao","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2797-0050","authenticated-orcid":false,"given":"Ang","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Liu, T., Jie, D., Zhuang, J., Zhang, D., and He, J. 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