AI Enabled Smart Fridge Management System: A Computer Vision & OCR-Integrated Framework for Food Waste Reduction

Food waste at the household level remains one of the most critical global sustainability challenges. Despite the availability of commercial smart refrigerators, existing solutions primarily focus on convenience features such as internal cameras, touch displays, and remote access, while lacking scientifically grounded mechanisms for food-waste reduction. This research presents an AI Enabled Smart Fridge Management System (SFMS)—a software-first, intelligent framework designed to minimize domestic food waste through FEFO (First-Expired, First-Out) based inventory management, computer vision preprocessing, OCR-based expiry date extraction, and AI-driven recipe recommendations using the Spoonacular API. The proposed system follows a four-layer IoT architecture consisting of sensing, network, data-processing, and application layers. SFMS prioritizes items nearing expiration, generates predictive waste alerts, and transforms expiring ingredients into actionable meal recommendations. Unlike existing commercial smart fridges, the system emphasizes waste intelligence, freshness analysis, behavioral learning, and sustainability-oriented decision making. Evaluation through simulations and early-stage computer vision and OCR prototypes demonstrates the technical feasibility of the approach, significant reduction in manual user effort, and strong potential for minimizing household food spoilage. The framework is designed to be scalable, extensible, and independent of expensive hardware, making it suitable for real-world domestic adoption. This work establishes a robust foundation for future smart kitchen ecosystems, with scope for deep-learning-based freshness detection, IoT sensor integration, mobile deployment, and community-level food redistribution networks.