Artificial Intelligence In The Food Industry: A Review Of Quality Inspection, Spoilage Prediction, Demand Forecasting And Traceability

The food industry must produce safe, fresh and affordable food for a growing population while reducing waste and cost, yet roughly one-third of food produced is lost or wasted. Many operations such as sorting, inspection, storage monitoring and demand planning are still performed manually, which makes them slow, inconsistent and dependent on human experience. This paper reviews how Artificial Intelligence supports food producers, processors, retailers and restaurants. It examines computer vision and convolutional neural networks for quality grading and defect inspection, electronic noses and IoT sensors combined with machine learning for spoilage detection and shelf-life prediction, time-series forecasting for demand planning, and digital traceability, including blockchain pilots, for food safety. Classical neural-network applications in food science and recent deep learning reviews are surveyed. A five-layer AI food management architecture with image capture, sensor monitoring, analytics modules, alerts and a traceability dashboard is proposed, with research gaps, objectives, a comparison of techniques and a discussion of challenges including variable natural products, data scarcity, hygiene constraints and cost for small enterprises. The review concludes that AI can improve consistency, reduce waste and strengthen food safety across the supply chain.