IoT-Driven Food Management System for Freshness Tracking and Predictive Analytics

Food quality monitoring and freshness tracking are important for reducing food wastage and ensuring food safety in smart storage and management systems. This project presents an IoT-Driven Food Management System for Freshness Tracking and Predictive Analytics using sensor monitoring, RFID technology, and machine learning techniques. The proposed system uses an Arduino microcontroller integrated with DHT11 and gas sensors to monitor environmental conditions such as temperature, humidity, and gas levels for food quality assessment. An RFID reader and RFID cards are used to identify food items, where each RFID card represents a predefined food product stored in the system database. When a food item is scanned, its manufacturing date, expiry date, and related details are displayed on the LCD screen and uploaded through IoT using the NodeMCU module for remote monitoring and data tracking. Machine learning techniques are used for food freshness prediction and analysis based on environmental conditions and stored food data. A buzzer alert mechanism is included to indicate expired food items during scanning, helping users identify unsafe or spoiled products. The proposed system improves food safety, supports freshness monitoring, reduces food wastage, and enables smart food management through IoT and predictive analytics technologies.