Artificial intelligence in food system: Innovative approach to minimizing food spoilage and food waste

Globally, about one-third of all food produced for human consumption is lost or wasted, compounding issues of food security, economic inefficiency, and environmental harm. Artificial Intelligence (AI) presents transformative potential to mitigate these losses by enhancing food spoilage predictions and optimizing supply chain management. This paper examines the deployment of AI technologies such as machine learning models, predictive analytics, and advanced algorithm in predicting food spoilage with high accuracy, thereby reducing food waste substantially. Key innovations highlighted include early detection systems for spoilage indicators, dynamic algorithms for optimal storage conditions, and predictive models for waste forecasting based on real-time environmental data. A review of case studies, including AI-driven solutions from Shelf Engine and Afresh, shows a 14.8 % reduction in food waste per store, with an associated reduction of 26,705 tons of CO 2 emissions. Similarly, IKEA achieved a 30 % reduction in kitchen food waste within one year using AI-powered monitoring systems. Despite these successes, challenges in data collection, model training, and the integration of AI into existing food management systems persist. These include issues related to data quality, legacy system compatibility, and regulatory barriers. The paper concludes with actionable recommendations for future research, urging interdisciplinary collaboration to develop standardized data protocols, enhance real-time monitoring capabilities, and address the ethical implications of AI adoption in the food sector. By advancing these strategies, AI's full potential in curbing global food waste can be realized. AI in Food Spoilage: Reducing Waste Efficiently. • AI Challenges: Data quality, privacy issues, biases, and system integration limit AI in spoilage prediction. • Proposed Solutions: Data standardization, bias reduction, federated learning, and model monitoring address challenges. • AI Models: Supervised, unsupervised, deep learning, & reinforcement learning offer unique benefits for spoilage prediction. • Federated Learning: Enables secure, decentralized data sharing, addressing privacy concerns in AI models. • Future Outlook: AI, IoT, and blockchain integration will enhance spoilage prediction and sustainability.