Review of Integration of Artifical Intelligence and Iot in Precision Livestock Farming
Keywords:
Smart Farming, Precision Agriculture, Livestock Monitoring, Machine Learning, Sensor NetworkAbstract
Traditional livestock management faces growing challenges in efficiency, animal welfare, and environmental sustainability. The integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies offers transformative opportunities for precision livestock farming through real-time monitoring, predictive analytics, and automated decision-making. This systematic review, using the PRISMA methodology, analyzed 62 peer- reviewed studies published between 2023 and 2025 across four domains: (1) AI-based disease detection and health monitoring, (2) IoT-driven feeding and nutrition management, (3) reproductive and behavioral monitoring, and (4) integrated AI–IoT farm management systems. Results show that machine learning models achieve 85– 95% accuracy in early disease detection, while IoT sensor networks reduce feed waste by 20–35% through optimized distribution. Smart platforms also improve reproductive efficiency by 15–25% and lower mortality rates by 12–18% in various livestock species. Despite these benefits, challenges remain in terms of high investment costs, data security, system interoperability, and limited farmer technical capacity. Smallholders are particularly affected by these barriers. Future research should prioritize low-cost sensor innovations, strong data governance frameworks, and user-friendly interfaces. Overcoming these obstacles is crucial to make precision livestock technologies accessible, equitable, and sustainable, enhancing productivity while safeguarding animal welfare and the environment.
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Copyright (c) 2026 Novantyo, M.F., Rahmawati, O, Makkah, K, Dynata, A.R, , Busa, F.T.B, Wijoyo,I.A,, Nurdianti
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