Artificial intelligence and machine learning in agri-food systems: A comprehensive review of technological frontiers, socio-ethical barriers, and future roadmaps

Abstract

Economic prosperity and human health heavily depend upon a stable food supply chain in agriculture. This essential network is fortified by modern farming techniques through boosted productivity and durability. Novel approaches are necessitated by the escalating worldwide need for nutrition. Consequently, agricultural practices and food security are positioned to be profoundly transformed by Artificial Intelligence (AI) and Machine Learning (ML). In this review, cutting-edge applications of AI in agriculture were investigated. A specific focus on precision crop management, optimized resource use, climate-resilient farming systems, and advanced livestock monitoring was undertaken. The article highlights how operational efficiency and decision-making are enhanced across all phases of any agricultural production system, such as planting, irrigation, pest control, harvesting, aquaculture, food safety, and post-harvest handling. This efficiency is attributed to the massive, heterogeneous datasets usually evaluated by AI frameworks to produce practical conclusions. Despite this promise, critical hurdles must be resolved to unlock the complete capabilities of AI in agriculture. Such barriers involve restricted access to high-quality data alongside computational limitations in developing regions. Serious ethical concerns regarding data privacy, algorithmic bias, and labor market disruptions also demand immediate attention. The deliberate mitigation of these complications is required to unlock the full potentials of AI for building a sustainable, fair, and food-secure tomorrow.