Artificial Intelligence–Based Food Shelf-Life Prediction
Keywords:
Remaining shelf life, Fresh produce, Perishable food monitoring, Machine learning, Predictive modeling, Internet of Things (IoT), Real-time sensor data, Cold chain logistics, Food quality assessment, Supply chain management, Food waste reduction, Transportation conditionsAbstract
This work introduces an innovative method for assessing the remaining shelf life of fresh produce during transit, fulfilling a vital requirement in the food supply chain. The suggested system utilizes modern data analytics and machine learning to integrate environmental parameters, including temperature, humidity, and transportation conditions, in order to forecast the deterioration of freshness and nutritional quality of perishable commodities over time. The system evaluates the deterioration rate of fresh produce and determines its remaining shelf life by evaluating real-time sensor data from IoT devices deployed in transportation vehicles. The system employs historical data and predictive modeling to consider fluctuations in transportation conditions and product attributes, hence improving the precision and dependability of shelf life assessments. This project seeks to validate the efficacy and practicality of the suggested system in enhancing supply chain management, minimizing food waste, and guaranteeing the delivery of high-quality fresh produce to consumers through extensive experimentation.
