The integration of Artificial Intelligence (AI) into cattle farming heralds a transformative shift toward precision livestock farming (PLF), promising unprecedented gains in productivity, animal welfare, and environmental sustainability. This research provides a comprehensive analysis of the opportunities and challenges inherent in deploying AI technologies across dairy, beef, and integrated agricultural systems. We review and synthesize data from current applications, including computer vision for behaviour and health monitoring, acoustic sensors for respiratory and feeding analysis, predictive analytics for disease outbreak forecasting, and optimization models for feed efficiency and methane emission reduction. The findings indicate that AI-driven systems can improve milk yield by 10-15%, reduce antibiotic usage through early disease intervention by up to 30%, enhance reproductive efficiency, and decrease feed costs by optimizing rations dynamically. However, significant challenges impede widespread adoption. These include high initial capital investment, data scarcity and the need for robust, farm-specific datasets, algorithmic transparency (“black box” issues), cybersecurity risks, and a pronounced digital skills gap among farmers. Furthermore, ethical considerations regarding data ownership, animal autonomy, and potential job displacement require scrutiny. The discussion concludes that while AI integration is not a panacea, its strategic implementation, supported by improved digital infrastructure, farmer-centric training, and responsive policy frameworks, can drive a more resilient, efficient, and sustainable cattle industry. Success depends on collaborative efforts among technologists, agronomists, veterinarians, and policymakers to ensure these technologies are accessible, understandable, and economically viable for farmers of all scales.

agriculture, artificial intelligence, cattle farming, opportunities, challenges

agronomy

Presentation: poster