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Big data and artificial intelligence in animal nutrition: a new era of precision feeding
Summary
This review paper rounds up recent research showing how AI and big data are helping farmers feed livestock more precisely—cutting waste, costs, and pollution while keeping animals healthy. One notable finding: AI tools can now detect microplastics in poultry feed with over 96% accuracy, which matters because contaminants in animal feed can potentially make their way into the meat, eggs, or dairy we eat. Overall, smarter livestock nutrition could mean more sustainable, lower-emission food production without sacrificing food safety or quality.
The convergence of Big Data and Artificial Intelligence (AI) is redefining animal nutrition by enabling precision feeding systems that are individualized, data-driven, and sustainability-oriented. This review synthesizes recent advances in multi-omics technologies, sensor-based monitoring, and machine learning applications across feed formulation, health surveillance, and production optimization. Precision feeding in pigs has been shown to reduce production costs by more than 8%, decrease protein and phosphorus intake by approximately 25%, lower nutrient excretion by up to 40%, and reduce greenhouse gas (GHGs) emissions by 6%, while maintaining or improving performance. In dairy systems, precision feed management strategies have achieved approximately 9.7% lower dietary crude protein levels, 14% reductions in manure nitrogen excretion, and annual net income gains of USD 137 per cow. AI-driven models have enhanced prediction of milk yield, feed conversion ratio (R² = 0.74), and residual feed intake (R² = 0.76), while enabling 96.26% accuracy in detecting microplastics in poultry feed. Integration of genomic, phenotypic, and sensor-derived datasets supports real-time monitoring, with wearable and IoT technologies transforming livestock management through continuous tracking of feeding behavior, emissions, and welfare indicators. Despite significant progress, current systems remain constrained by data heterogeneity, limited interoperability, and insufficient prescriptive decision-support frameworks. This article identifies methodological, technological, and adoption-related gaps, while highlighting future directions including nutrigenomics- and metagenomics-informed diet design, adaptive precision nutrition, and cost-effective solutions for smallholder systems. Collectively, these innovations establish Big Data and AI-enabled precision nutrition as a cornerstone of sustainable livestock production, advancing food security, climate resilience, and ethical animal management.