Artificial Intelligence and Internet of Things in Food Supply Chain Sustainability: A Data Driven Approach to Waste Reduction, Predictive Quality Management, and Circular Economy Integration

Authors

  • Ebrahim Alinia Ahandani

Abstract

Food loss and waste (FLW) represent one of the most paradoxical failures of the modern food system: roughly one‑third of all food produced for human consumption – approximately 1.3 billion tonnes annually – is lost or wasted, while 828 million people face chronic hunger. This review provides a comprehensive and critical synthesis of how Artificial Intelligence (AI) and Internet of Things (IoT) technologies are being deployed across the food supply chain (FSC) to monitor, predict, and reduce FLW. We systematically evaluate 85 peer‑reviewed studies (2020–2026) covering the entire chain from primary production to retail and consumer behaviour. Specific applications include: (i) AI‑powered yield prediction and precision harvesting in agriculture; (ii) IoT‑based cold chain monitoring with real‑time quality degradation modelling; (iii) computer vision for dynamic expiry date prediction and automated sorting in food processing; (iv) AI‑driven demand forecasting and inventory optimisation in retail; and (v) consumer‑facing smart storage and digital nudging. We critically examine the technical barriers – sensor cost, data interoperability, model generalisation across food types – as well as organisational and behavioural challenges. Integration of nanotechnology‑based sensors (e.g., for ethylene or volatile amines) with IoT platforms is highlighted as an emerging frontier. Life cycle assessment (LCA) of AI/IoT interventions shows that FLW reduction of 25–40% is achievable, with corresponding decreases in GHG emissions, water use, and land pressure. Finally, we discuss the role of circular economy principles and the need for open data standards. Our conclusion is that AI and IoT are no longer optional but essential for a resilient, low‑waste food system, provided that implementation costs are addressed and data governance frameworks are established.

Published

20-06-2026

How to Cite

Ahandani, E. A. . (2026). Artificial Intelligence and Internet of Things in Food Supply Chain Sustainability: A Data Driven Approach to Waste Reduction, Predictive Quality Management, and Circular Economy Integration. 8th International Anatolian Agriculture, Food, Environment and Biology Congress, Sinop/Türkiye. from https://targid.indac.com.tr/index.php/TURSTEP/article/view/957