Improved Food Supply Chain Forecasting Management Using IoT and Machine Learning

Consumer health awareness was extremely important to the world today. In the production, preparation and distribution process, contamination can occur at any time. The perishable food supply therefore needs to pay close attention to food efficiency and safety. The health and quantity of food was assured by the traceability system, as it provides comprehensive food data. The IoT-based surveillance program proposed in this study employed Radio Frequency Identification (RFID) and Raspberry Pi-based devices. The goods were tracked and traced using an RFID reader, while humidity and temperature were monitored during production and transportation using a Raspberry Pi. In addition, a prediction based on Machine Learning (ML) was used to estimate future values so that the device could issue an advanced warning if expected temperatures exceed the threshold range. The results showed that the proposed method was capable of tracking products and accurately and successfully forecasting sensor information compared to previous approaches.