An Intelligent Multi-Modal Food Quality Monitoring System Using IoT and Machine Learning

This paper presents FoodQ, a proper monitoring of storage in warehouses and containers is crucial for quality maintenance and loss prevention. The present work, therefore, addresses the issue of de- velopment of an intelligent system for food ware- house and shipping container monitoring utilising Internet of Things and Machine Learning-based technologies. In addition to this, the system com- bines devices from the Internet of Things—sensors of temperature, humidity, and gas levels that con- tinuously remain on record to monitor food storage conditions. These sensors receive data, which is fed to machine learning models for quality testing to identify the item as fresh or spoiled. Real-time vi- sualization of data by the web-based dashboard and automated notifications at the time of violating pre- defined threshold conditions, provides timely inter- ventions. The dashboard is essentially web-based, remotely accessible via a mobile application, and thus users of the system are able to monitor food in real-time from anywhere in the world. This in- telligent monitoring solution is going to provide as- sistance to warehouseb management and container tracking, highly perceptible in decreasing losses and in increasing efficiency related to food storage. This research will provide a solution that is cost-saving and reliable, with extensive use in the food storage and logistics industry in terms of quality and safety and on food loss and waste minimisation.