FoodSavior: Distributing Surplus Food to NGOs or Manure Producers Using Internet of Things and Machine Learning

By evaluating the quality of leftover food and enabling its best possible redistribution, this research presents a novel approach to addressing food waste in the restaurant sector. The system uses a Flutter an Dart-based application to control the food distribution process and hardware sensors like MQ4 to identify gasses that indicate spoiling. While spoiled food is sent to manure producers for use in agriculture, non-spoiled food is sent to NGOs to feed people in need. In order to minimize waste, the system analyzes sensor data and uses machine learning algorithms, such as Decision Trees, to forecast spoiling patterns and suggest preparation techniques. Restaurants can quickly enter food data into the app, which NGOs and manure producers may use to collaborate in real time. This data is processed by the Django-developed backend server, and Arduino C++ is used to control hardware activities. This solution promotes sustainability, social responsibility, and community welfare by facilitating a smooth collaboration between farmers, restaurants, and non-governmental organizations. The method helps create a more sustainable and just food environment by decreasing food waste and improving resource efficiency. This version emphasizes the objectives, innovation, and societal impact of the work while maintaining technical details.