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Computer Vision in Dairy Farm Management: A Literature Review of Current Applications and Future Perspectives
Department of Agricultural and Food Sciences (DISTAL), University of Bologna, 40127 Bologna, Italy
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Animals 2025, 15(17), 2508; https://doi.org/10.3390/ani15172508
Submission received: 17 July 2025
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Revised: 22 August 2025
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Accepted: 25 August 2025
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Published: 26 August 2025
(This article belongs to the Special Issue Nutritional and Management Strategies for Heat-Stressed Ruminants)
Simple Summary
Computer vision is becoming a transformative tool in dairy farm management, offering farmers a more efficient, accurate, and non-invasive way to monitor herd health, behavior, and productivity. This enables real-time decision-making, reduces labor costs, and minimizes errors associated with manual observation. Additionally, computer vision systems can enhance animal welfare by identifying stress or discomfort early, leading to timely interventions that improve overall herd well-being and productivity. When integrated with other smart farming technologies, such as automated milking systems and precision feeding, computer vision contributes to sustainable and data-driven farm management, optimizing milk yield while reducing waste and resource use. Ultimately, the adoption of computer vision in dairy farming supports both economic efficiency and animal welfare, aligning with the growing demand for sustainable agricultural practices.
Abstract
Computer vision is rapidly transforming the field of dairy farm management by enabling automated, non-invasive monitoring of animal health, behavior, and productivity. This review provides a comprehensive overview of recent applications of computer vision in dairy farming management operations, including cattle identification and tracking, and consequently the assessment of feeding and rumination behavior, body condition score, lameness and lying behavior, mastitis and milk yield, and social behavior and oestrus. By synthesizing findings from recent studies, we highlight how computer vision systems contribute to improving animal welfare and enhancing productivity and reproductive performance. The paper also discusses current technological limitations, such as variability in environmental conditions and data integration challenges, as well as opportunities for future development, particularly through the integration of artificial intelligence and machine learning. This review aims to guide researchers and practitioners toward more effective adoption of vision-based technologies in precision livestock farming.
Keywords:
cow; PLF; deep learning; heat stress; machine vision
