AI and blockchain in agribusiness: Transformative applications in supply chain management

Abstract:

The agribusiness supply chain faces persistent challenges including seasonality, perishability, food waste, and quality variability. This review examines the transformation of agribusiness supply chain management through artificial intelligence (AI) technologies, drawing on case studies from predictive analytics in dairy procurement, quality control automation, supply chain optimization platforms, precision agriculture, blockchain traceability, and vaccine cold chain management. This paper demonstrates how AI-driven solutions significantly improves operational efficiency, reduce waste, and enhance transparency. The study employs a systematic literature review methodology supplemented by real-world implementations. Findings indicate that AI technologies such as machine learning, computer vision, and predictive analytics substantially improve demand forecasting, inventory management, and decision-making. However, challenges including infrastructure deficiencies, high initial costs, cybersecurity risks, and workforce skill gaps require strategic attention. Future integration of AI in agribusiness supply chains demands balanced approaches combining technological advancement with human-centric practices and sustainability objectives. This review provides comprehensive guidance for academic researchers, practitioners, and policymakers seeking to optimize agribusiness supply chain operations through AI technologies.