Vision-based livestock pose estimation for precision livestock farming: a systematic review and design framework

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dc.contributor.authorMenolotto, Matteo
dc.contributor.authorTedesco, Salvatore
dc.contributor.authorO’Grady, Luke
dc.contributor.authorKennedy, Emer
dc.contributor.authorO'Flynn, Brendan
dc.contributor.funderScience Foundation Ireland (SFI), SFI, 12/RC/2289-P2-INSIGHT-SFI 21/RC/10303
dc.date.accessioned2026-06-26T11:50:02Z
dc.date.available2026-06-26T11:50:02Z
dc.date.issued2026-09-01
dc.description.abstractCamera-based pose estimation is becoming a key sensing approach for automated livestock monitoring, yet the selection of camera geometry, keypoint schema, learning model, and deployment strategy remains highly heterogeneous across species and applications. This systematic review synthesised the literature published or available online between 1 January 2015 and 31 December 2025 to identify task-aligned design patterns, reporting gaps, and practical routes toward on-farm implementation. Searches of ScienceDirect, Scopus, Web of Science, and Embase identified 273 records. After duplicate removal, eligibility screening, and methodological quality appraisal, 114 studies were included in the final synthesis. The reviewed literature shows that cattle and pigs dominate the field, while poultry, horses, goats, sheep, camels, and cross-species studies remain comparatively under-represented. Across applications, side-view RGB is most consistently suited to gait and lameness analysis, whereas fixed top-down RGB offers the best trade-off for group-level behaviour, feeding, tracking, and identity-related tasks. Stereo, RGB-D, and multi-view systems are most justified when the target output depends on absolute geometry, persistent occlusion handling, or three-dimensional posture recovery, particularly in body measurement and weight-estimation pipelines. The synthesis further shows that performance depends more on task-camera-keypoint alignment than on any single model family. However, progress toward robust deployment is limited by inconsistent landmark definitions, fragmented datasets, heterogeneous evaluation metrics, and insufficient reporting of runtime, calibration burden, and farm-system integration. A task-oriented reporting structure and preliminary species-specific core keypoint guidance are therefore proposed to improve comparability and reuse. Overall, livestock pose estimation is sufficiently mature for several structured farm applications, but broader adoption will depend on stronger standardisation, more diverse multi-site datasets, and more explicit validation under real operational conditions.en
dc.description.sponsorshipThis paper has emanated from research funding provided by Science Foundation Ireland (SFI) which is Co-Funded through the European Regional Development Fund under Grant 12/RC/2289-P2-INSIGHT, and the SFI Centre VistaMilk (SFI 21/RC/10303).
dc.description.versionPublished Version
dc.format.extent32
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid111951
dc.identifier.authororcidMenolotto, Matteo
dc.identifier.authororcidTedesco, Salvatore§0000-0002-7752-2240
dc.identifier.authororcidO’Grady, Luke
dc.identifier.authororcidKennedy, Emer
dc.identifier.authororcidO'Flynn, Brendan§0000-0002-5522-2597
dc.identifier.citationMenolotto, M, Tedesco, S, O’Grady, L, Kennedy, E & O'Flynn, B 2026, 'Vision-based livestock pose estimation for precision livestock farming: a systematic review and design framework', Computers and Electronics in Agriculture, vol. 251, 111951, pp. 1-32. https://doi.org/10.1016/j.compag.2026.111951
dc.identifier.doi10.1016/j.compag.2026.111951
dc.identifier.endpage32
dc.identifier.issn0168-1699
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/10468/18974
dc.identifier.volume251
dc.language.isoen
dc.publisherElsevier B.V.
dc.relation.urihttps://www.scopus.com/pages/publications/105041114041
dc.rights© 2026, the Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ).
dc.rights.accessrightsopen access
dc.rights.licensenameAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.statusPeer reviewed
dc.subjectAnatomical landmark detection
dc.subjectAnimal welfare monitoring
dc.subjectBody measurement
dc.subjectLameness detection
dc.subjectMulti-view imaging
dc.subjectOn-farm monitoring
dc.subject[TyndallMicroNano]
dc.titleVision-based livestock pose estimation for precision livestock farming: a systematic review and design frameworken
dc.typeReview
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