A procedure for tracing supply chains for perishable food based on blockchain, machine learning and fuzzy logic

Abstract

One of the essential points of food manufacturing in the industry and shelf life of the products is to improve the food traceability system. In recent years, the food traceability mechanism has become one of the emerging blockchain applications in order to improve the anti-counterfeiting area’s quality. Many food manufacturing systems have a low level of readability, scalability, and data accuracy. Similarly, this process is complicated in the supply chain and needs a lot of time for processing. The blockchain system creates a new ontology in the traceability system supply chain to deal with these issues. In this paper, a blockchain machine learning-based food traceability system (BMLFTS) is proposed in order to combine the new extension in blockchain, Machine Learning technology (ML), and fuzzy logic traceability system that is based on the shelf life management system for manipulating perishable food. The blockchain technology in the proposed system has been developed in order to address light-weight, evaporation, warehouse transactions, or shipping time. The blockchain data flow is designed to show the extension of ML at the level of food traceability. Finally, reliable and accurate data are used in a supply chain to improve shelf life.