Real Time Food Monitoring and Quality Alert System Using IoT and Streamlit

The food industry has experienced tremendous growth over the past decade, which has resulted in a growing concern about food safety. With the increased demand for food, the risk of contamination and spoilage has also increased, leading to significant public health and economic consequences. In order to protect food from contamination during transportation caused by environmental factors, this paper suggests an IoT framework for easy food monitoring. A prototype with an alert system is designed where sensors are used to detect the quality of food at various environmental conditions across the supply chain. Experimentations are carried out using temperature, humidity, and gas conditions, providing the limits of the storage conditions as 34 °C, 77 (% rh), and 4000 ppm respectively. The sensor data is processed and analyzed to create a food condition predictive model using K-Means and KNN with an accuracy of 78%. Novel Visualizations and a dynamic food quality graph are displayed over an interactive Streamlit website and Thingspeak cloud server achieving real time data communication with the user. The results show its relevance in the field of food logistics.