Title: Farm-Scale Autonomous Welfare Monitoring in Precision Livestock: A Systematic Review of Robotics and Multimodal AI with an Emphasis on the Lab-to-Farm Deployment Gap
Primary area: applications to robotics, autonomy, planning
Keywords: precision livestock farming, dairy cattle, animal welfare monitoring, autonomous navigation, computer vision, multimodal AI, edge computing, technology adoption, systematic review, welfare representation learning

Abstract: While breakthroughs in autonomous robotics and multimodal artificial intelligence (AI) promise continuous, real-time monitoring for precision livestock farming, their practical on-farm application faces significant limitations, revealing a critical lab-to-farm deployment gap. The primary objective of this systematic review is to highlight state-of-the-art knowledge on these technologies to understand and bridge this gap, proposing a path forward that benefits both agricultural practice and on-farm research. From a pool of over 900 articles reviewed on autonomous navigation and AI-driven analytics of physical and physiological welfare data, we systematically selected 33 studies to propose recommendations for adopting farm-scale autonomous monitoring in precision livestock. Our review reveals that while foundational technologies are well-established, research remains fragmented. Studies are often limited to laboratory simulations in lab experiments or small, single-farm field trials that lack generalizability to real-world operational conditions. Key challenges identified include edge-compute latency, poor model generalization across diverse environments, and the absence of standardized validation benchmarks. Based on these findings, we propose a deployment-oriented roadmap with recommendations for developing and adopting integrated, robust, and scalable systems to accelerate the ethical and effective adoption of these technologies across the dairy industry. Furthermore, we pinpoint a critical deficiency, that is, the lack of a standardized learning representation for collected welfare insights. This gap prevents the creation of a useful knowledge base for the embodied AI in robotics scientific community, hindering the development of robust models. We further assert the need to translate welfare insights into standardized, learnable representations, thereby creating the reproducible datasets required for training the next generation of robust models for livestock welfare.

TL;DR: We systematically reviewed autonomous cattle monitoring and found that the key challenge is no longer developing foundational technology, but ensuring it can be reliably and scalably deployed on real farms.