SMART FOOD WASTE MANAGEMENT USING MACHINE LEARNING AND IoT

The study proposes a new intelligent system of food waste management (Smart Food Waste Management System - SFWMS) that combines the latest technologies of the Internet of Things (IoT) and Machine Learning (ML) to improve the efficiency of traditional methods that are currently being used to address the growing global epidemic of food waste. The system uses IoT-enabled sensors to monitor real-time data on the amount of food, where it’s stored, and how much goes to waste in a variety of places (e.g., homes, hostels, restaurants) and transmit this data to a common cloud-based platform. It also includes historical and real-time data from both sources that can be used with machine-learning algorithms to predict future food waste patterns. The SFWMS uses predictive analytics to forecast peak waste times and make data-driven recommendations for optimal distribution, reuse and/or disposal of food. Additionally, the SFWMS sends automated alerts and uses a dashboard that is easy to use to facilitate timely decisions and support performance monitoring while providing enhanced efficiency and scalability compared to existing food waste management systems. Through a proactive and data-driven approach, the SFWMS system enables the efficient use of food resources by utilising predictive analytics to reduce unnecessary food waste. The study presents the findings of a pilot project and highlights that the combination of IoT and ML technologies can greatly reduce food waste, have a positive impact on the environment and provide sustainable practices. This project contributes to the development of intelligent, scalable, and environmentally friendly food waste management solutions for smart cities and modern food systems.