Advancing Livestock Facial Recognition with AI: From Algorithm Innovation to End-to-End Precision Farming Application

Non-contact monitoring in precision livestock farming (PLF) needs reliable individual identification and face-anchored analytics to link animals with longitudinal health and behavior signals in variable barns. Evidence is fragmented across pipeline modules and deployment readiness is difficult to assess because robustness and operational KPIs are inconsistently reported. We map research evolution and synthesize deployment-oriented evidence and design principles. A two-stage review was conducted: CiteSpace bibliometric mapping of Web of Science Core Collection records (2005–2025; pre-2005 relevant records were sporadic), followed by a scoping synthesis of peer-reviewed empirical studies (2022–2025) searched mainly in ScienceDirect and supplemented by Web of Science, Scopus, IEEE Xplore, and CNKI. We included studies using livestock facial imagery (RGB and/or thermal/IR) for identity functions or face-coupled ROI analytics with quantitative cohort evaluation. Following QRD screening, 24 studies were retained. We consolidate deployment factors and reporting gaps and propose “Digital Individuals” as persistent identity anchors for multimodal longitudinal records and closed-loop decision support.