IoT Based Food Spoilage Detection using Machine Learning Techniques

Food rotting is an essential concern contributing to significant food waste, and novel early detection and prevention methods are needed. Existing manual monitoring procedures are generally time-consuming and prone to errors, resulting in spoiling detection delays. The study presents an IoT-based food spoilage detection system to solve these issues. Using IoT sensors and machine learning (ML) algorithms, the proposed System provides continuous monitoring and predictive analysis of essential variables such as temperature, humidity, gas levels, and food availability. The proposed System uses real-time monitoring and alerting to enable proactive interventions to prevent spoilage, decrease waste, and promote sustainability throughout the food supply chain. The performance evaluation shows that the proposed System outperforms existing systems in terms of accuracy (0.92), precision (0.90), recall (0.89), and F1-score (0.91). Overall, the IoT-based Food Spoilage Detection System provides an integrated strategy for reducing food waste and increasing sustainability, representing a big step forward in food waste reduction initiatives.