Smart Food Monitoring System Based on IoT and Machine Learning

The monitoring of food quality is crucial in ensuring food safety and reducing food waste. In this paper, we present the design and implementation of a smart food monitoring system that utilizes Internet of Things (IoT) and Machine Learning (ML) technologies. The system includes IoT devices such as temperature and humidity sensors, as well as gas concentration sensors, to continuously monitor food items. The data collected by these sensors is analyzed using machine learning algorithms to determine the freshness of the food. The system also features a web-based dashboard that displays real-time data and alerts when the food items reach a certain threshold level. The dash-board can be accessed remotely using a mobile app, making it easy for users to monitor their food from anywhere. The system's effectiveness was evaluated using various food items, and the results showed that it successfully predicted the freshness of food with high accuracy. The proposed system has significant potential applications in the food industry, reducing food waste and ensuring that consumers can access fresh and healthy food. Additionally, the system provides an efficient way of tracking food inventory, reducing costs and increasing efficiency. The system's real-time monitoring and alerts are ideal for personal use in homes and kitchens. In conclusion, the proposed smart food monitoring system using IoT and machine learning provides a reliable and cost-effective solution for monitoring the freshness of food. The system has significant potential applications in the food industry and personal use, reducing food waste, increasing efficiency, and ensuring access to fresh and healthy food.