Improving efficiency of RFID-based traceability system for perishable food by utilizing IoT sensors and machine learning model

Abstract

Radio Frequency Identification (RFID) technology has significantly improved in the past few years and is presently sought for implementation in the identification and traceability of perishable food in the food sector to safeguard food safety and quality. It is currently considered a worthy successor to the barcode system and has significant advantages for monitoring products in the perishable food supply chain (PFSC). The present study proposes a traceability system that utilizes RFID and Internet of Things (IoT) sensors. RFID technology can be used to track and trace perishable food while IoT sensors can be used to measure temperature and humidity during storage and transportation. Furthermore, it is important that RFID gates can identify the direction of tags and whether products are being received or shipped through the gate. In this study, machine-learning models are utilized to detect the direction of passive RFID tags. The input features are derived from receive signal strength (RSS) and the timestamp of tags. The proposed system has been tested in the perishable food supply chain and has revealed significant benefits to managers and customers by providing real-time product information and complete temperature and humidity history. In addition, by integrating a machine-learning model into the RFID gate, tagged products that move in or out through a gate can be correctly identified and thus improve the efficiency of the traceability system.