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The agricultural sector, particularly animal production, faces numerous unprecedented challenges driven by climate change, resource depletion, and an ever-growing global demand for quality food. These challenges are further compounded by the increasing environmental impact of livestock farming, including greenhouse gas emissions, overuse of water and land resources, and the destruction of vital ecosystems. Ensuring the sustainability of animal production systems while mitigating the negative environmental impacts of these factors is essential for future global food security. As the demand for animal-derived products continues to rise, there is a pressing need for innovations that can enhance productivity without compromising environmental integrity or animal welfare. Artificial intelligence (AI) has emerged as a transformative technology with the potential to revolutionize the animal production industry. AI-driven solutions offer promising avenues for optimizing production efficiency, enhancing animal health and welfare, and reducing the environmental footprint of livestock farming. Machine learning, sensor technologies, and advanced data analytics are being increasingly utilized to monitor and predict various aspects of animal farming, such as feed efficiency, disease prevention, and climate resilience. These technologies enable farmers to make data-driven decisions, fostering more sustainable and environmentally responsible practices. This review examines the integration of AI into animal production systems, emphasizing its applications in climate change mitigation, resource management, and advancing sustainability. The discussion addresses how AI technologies can be utilized to improve productivity while minimizing environmental impact and enhancing animal welfare. Additionally, the paper outlines future opportunities, challenges, and potential barriers to integrating AI technologies into livestock farming, thereby ensuring long-term sustainability amid global challenges.
Abdel-Wareth et al. (Sat,) studied this question.