Effective food quality monitoring has been a long-standing challenge for the food industry, starting from the time it was developed in 1988 in Germany. It has progressed from just monitoring foods for undesirable substances and contaminants to preventing food spoilage and promoting food safety. With the emergence of IoT and machine learning technologies, food quality monitoring has become more efficient and accurate. These technologies enable real-time monitoring of food items, as well as data analysis to predict the freshness of food. Various studies have shown that they can accurately predict food freshness, making them a better solution for food quality monitoring.
This project aims to provide a remote, low-cost and non-destructive food quality monitoring system that uses an AS7265x multispectral sensor to assess actual food quality, temperature and humidity sensors for environmental factors and ML models to predict food spoilage risk. The system will run on open-source hardware with cloud connectivity for storage and remote access. Suppliers and logistics managers will be able to monitor the food being delivered remotely and receive automatic alerts when spoilage thresholds are predicted to be exceeded. Ultimately, it will reduce food waste, improve food safety, and assist in making data-informed decisions during the transportation of the food.
Honours Student
4229699@myuwc.ac.za
Supervisor
fisingizwe@uwc.ac.za
Co-Supervisor
cnyindera@uwc.ac.za