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Abstract
Modern dairy farming is undergoing a paradigm shift toward smarter, more sustainable livestock management driven by the integration of artificial intelligence (AI) and computer vision technologies. These innovations are transforming traditional practices, enabling more precise, efficient, and humane approaches to herd management. This review examines how AI technologies, particularly computer vision, machine learning, and sensor-based systems, are enhancing core areas of dairy operations, including cattle identification, health monitoring, disease detection, and reproductive management. Advanced image-based systems now enable contactless identification, improving animal welfare and operational precision. AI-enabled health surveillance tools support early disease detection, reducing veterinary costs and improving herd productivity. In reproductive care, AI facilitates accurate estrus detection and pregnancy monitoring using data from wearable sensors and cameras, optimizing insemination timing and calving outcomes. Integration with smart farm platforms also allows real-time decision-making for feeding, barn conditions, and logistics, thereby boosting profitability and environmental sustainability. Despite significant progress, challenges such as infrastructure gaps, high costs, and data governance remain. This review also proposes a roadmap for inclusive AI adoption and emphasizes the need for interdisciplinary education and ethical deployment. By synthesizing recent innovations and addressing critical barriers, the paper presents AI as an enabler of intelligent and efficient dairy farming. As global dairy demand rises, AI offers scalable solutions to improve productivity while supporting long-term environmental and animal welfare goals.
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Article Type: Review Article
Agricultural and Environmental Education, Volume 5, Issue 1, June 2026, Article No: em012
https://doi.org/10.29333/agrenvedu/18108
Publication date: 13 Mar 2026
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