AI Usage in Agriculture: Current Applications, Animal Science Perspective, and Future Trends

Abstract

Global population projections indicating a reach of 9.7 billion by 2050 have significantly magnified the pressure on limited arable land and freshwater resources, turning food security into a paramount strategic objective. Artificial Intelligence (AI) integration offers innovative solutions to chronic challenges faced by traditional agricultural practices, such as severe labor shortages, climate variability, and yield inefficiencies. This review paper synthesizes AI applications in modern agriculture, ranging from multi-source data collection and predictive machine learning models to crop monitoring, precision irrigation, and supply chain optimization. Special emphasis is dedicated to Animal Science (Zootechnics), showcasing thermal imaging-based Artificial Neural Network (ANN) algorithms for mastitis detection in dairy cattle, automated feeding technologies, and continuous welfare monitoring. Furthermore, venture capital investments in the EU agritech sector, labor force transitions, structural implementation barriers, and future projections are thoroughly analyzed.