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: 0000-00-00

Journal: TechRxive

Status: Published

DOI: https://doi.org/10.36227/techrxiv.177138881.15794631/

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While breakthroughs in autonomous robotics and multimodal artificial intelligence (AI) promise continuous, realtime monitoring for precision livestock farming, their practical on-farm application faces significant limitations, revealing a critical "lab-to-farm" deployment gap that is rooted in fundamental challenges to the embodied AI community: poor model generalization, simulation-to-real fragility, and the absence of standard validation benchmarks. This review highlights today's state of the art in order to understand and bridge the gap. Using a pool of over 900 reviewed articles, we selected 33 studies from 2021 to 2025 to propose recommendations for adopting farm-scale autonomous monitoring. Our review reveals that 67% of robotics research relies on simulation, with no validation in dynamic farm environments. Based on this finding, we propose a technical roadmap focused on three pillars: 1) the use of Generative AI for data standardization and sim-to-real adaptation; 2) the adoption of a "Leave-One-Farm-Out" protocol for rigorous field validation; and 3) 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.