LEVERAGING AI FOR OPTIMIZING FOOD PRESERVATION AND SHELF-LIFE PREDICTION IN SMART REFRIGERATION SYSTEMS

Abstract

Abstract: The integration of Artificial Intelligence (AI) into smart refrigeration systems represents a significant advancement in food preservation and shelf-life prediction. This research paper explores how AI can optimize food preservation by leveraging real-time data from IoT sensors and advanced machine learning algorithms. The study focuses on developing and implementing AI-driven models that dynamically adjust refrigeration conditions to maintain optimal storage environments, thereby enhancing food quality and extending shelf life. The framework involves the collection of real-time data on temperature, humidity, and other critical parameters from smart refrigeration units equipped with IoT sensors. Machine learning algorithms are employed to analyze this data, predict the remaining shelf life of food products, and make real-time adjustments to refrigeration settings. The study evaluates the performance of these AI models by comparing them with traditional refrigeration methods, assessing improvements in food preservation, spoilage reduction, and energy efficiency. Results indicate that AI-enhanced refrigeration systems significantly outperform traditional methods, providing more accurate shelf-life predictions and reducing food spoilage rates. The AI models achieved high accuracy, precision, and recall, resulting in effective optimization of refrigeration conditions and extended shelf life for perishable items. Additionally, the AI systems demonstrated improved energy efficiency by optimizing cooling levels and reducing compressor cycles. Comparative analysis shows that AI-driven systems offer substantial benefits over conventional methods, including better predictive accuracy and reduced operational costs. Practical applications of the AI-enhanced system were validated through case studies, highlighting its effectiveness in real-world settings and its potential for improving food quality and reducing waste. The study also identifies areas for further research, including algorithm refinement, user interface enhancements, and scalability. Overall, the integration of AI into smart refrigeration systems offers a promising approach to advancing food preservation practices and contributing to more sustainable food management.