Farm-Scale Autonomous Welfare Monitoring in Smart Livestock Farming: A Systematic Review of Robotics and Multimodal AI With an Emphasis on the Lab-to-Farm Deployment Gap

Authors: Francois Gonothi Toure and Abdoulaye Baniré Diallo and Mounir Boukadoum

Date: 2026-06-01

Journal: IEEE Transactions on AgriFood Electronics

Status: Published

DOI: 10.1109/TAFE.2026.3687491

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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 driven by fundamental challenges to the embodied AI in robotics community, including poor model generalization, simulation-to-reality fragility, and the absence of standard validation benchmarks. This review highlights today’s state of the art in autonomous robotics and AI to help understand and bridge the gap. From an initial pool of over 900 articles, we selected 33 studies from 2021 to 2025 to propose recommendations for adopting farm-scale autonomous monitoring. Our review reveals that about 67% of the robotics and autonomy research relies on simulation, with no validation in dynamic farm environments. Based on this finding, we propose a technical roadmap that focuses on three pillars: first, the use of Generative AI for data standardization and simulation-to-reality adaptation; second, the adoption of a “leave-one-farm-out” or LOFO protocol for rigorous field validation; and third, the development of Edge native systems. Furthermore, we define a standardized welfare Insight schema to facilitate the creation of reproducible datasets, thus enabling the development of truly robust models for livestock welfare.