RiceChain: secure and traceable rice supply chain framework using blockchain technology

PeerJ Comput Sci. 2022 Jan 12:8:e801. doi: 10.7717/peerj-cs.801. eCollection 2022.

Abstract

The increasing number of rice product safety issues and the potential for contamination have established an enormous need for an effective strategy for the traceability of the rice supply chain. Tracing the origins of a rice product from raw materials to end customers is very complex and costly. Existing food supply chain methods (for example, rice) do not provide a scalable and cost-effective means of agricultural food supply. Besides, consumers lack the capability and resources required to check or report on the quality of agricultural goods in terms of defects or contamination. Consequently, customers are forced to decide whether to utilize or discard the goods. However, blockchain is an innovative framework capable of offering a transformative solution for the traceability of agricultural products and food supply chains. The aim of this paper is to propose a framework capable of tracking and monitoring all interactions and transactions between all stakeholders in the rice chain ecosystem through smart contracts. The model incorporates a system for customer satisfaction feedback, which enables all stakeholders to get up-to-date information on product quality, enabling them to make more informed supply chain decisions. Each transaction is documented and stored in the public ledger of the blockchain. The proposed framework provides a safe, efficient, reliable, and effective way to monitor and track rice products safety and quality especially during product purchasing. The security and performance analysis results shows that the proposed framework outperform the benchmark techniques in terms of cost-effectiveness, security and scalability with low computational overhead.

Keywords: Agricultural supply chain; Ethereum blockchain; Food security and traceability; Rice production; Smart contract.

Grants and funding

This work was supported by the Artificial Intelligence Data Analytics Lab (AIDA) CCIS, Prince Sultan University, Riyadh, Saudi Arabia. Prince Sultan University also paid the article processing charges. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.