A Non-Fungible Token and Blockchain-Based Cotton Lint Traceability Solution

Author:

Wang Lixin1,Sun Wenlei1,Zhao Jintao1ORCID,Zhang Xuedong1,Lu Cheng1,Luo Hao1

Affiliation:

1. School of Mechanical Engineering, Xinjiang University, Urumqi 830047, China

Abstract

As a critical raw material for the textile industry, cotton lint provides various types of cotton yarns, fabrics and finished products. However, due to the complexity of the supply chain and its many links, information records are often missing, inaccurate or lagging, resulting in low transparency and traceability. In the traditional cotton lint supply chain, the data of each link are stored in isolation; due to the lack of an effective sharing mechanism and the formation of “information silos”, complete traceability is challenging to achieve. In addition, the completeness and authenticity of documents such as lint quality reports and certificates of origin must be rapidly strengthened. Otherwise, quality problems may arise. To solve the above problems, this study proposes a cotton lint supply chain traceability system based on blockchain and non-fungible tokens (NFTs), covering the whole cotton lint production process from harvesting to selling. We use an NFT as an asset token to digitise seed cotton, cotton lint and quality inspection reports and allow participants to store and manage these assets on the blockchain. The system design includes architecture diagrams, sequence diagrams and Ethernet smart contract development based on the ERC721 standard. In addition, the integration of Interplanetary File System (IPFS) technology solves the problem of storing large files on the chain and ensures that the data are permanently preserved and cannot be tampered with. We provide a diagram of the interactions between the system components and the four core algorithms’ design, testing and verification process. We present an in-depth analysis of the solution regarding the transaction costs and smart contract security. We confirm the solution’s security, reliability and applicability through a cost evaluation and security analysis.

Funder

Quality Tracing and Detection of Machine-picked Cotton Based on Industrial Internet Logo Resolution

Publisher

MDPI AG

Reference33 articles.

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