Blockchain-Powered Traceability for Safer Grain and Oil Food Supply Chains

By Dr Silpaja Chandrasekar, PhDReviewed by Susha Cheriyedath, M.Sc.Aug 30 2023

In a paper published in the journal Foods, researchers addressed the intricate grain-and-oil-food-supply chain by introducing a blockchain-based traceability model, enhanced by a blockchain-machine learning combo for reliable data. The intricate grain-and-oil-food-supply chain is known for its complexity and cross-regional aspects. Emerging challenges like mixed stock, pricing pressure, and counterfeit items prompted an exploration of blockchain's potential.

In this study, a lightweight storage approach and data recovery mechanism were introduced that reduced pressure and enhanced fault tolerance. Results showcased swift query times and efficient data recovery. The researchers built a Hyperledger Fabric-based traceability system, outperforming existing setups, crucially ensuring grain-and-oil-food safety in China.

Background

China, a significant agricultural player, depends on grain for both nourishment and security, with its nutritional value encompassing minerals, fiber, and essential nutrients. However, incidents involving contaminated rice and wheat have heightened worries about grain quality.

In grain trade, challenges such as surplus accumulation, quality control, and consumer trust further complicate matters. The intricate grain-and-oil-food supply chain, marked by its complexity, multi-stage processes, and diverse data sources, intensifies traceability issues related to data accuracy, centralization, and potential tampering. These complexities hinder the swift identification of accountability during safety concerns, and concurrently, the efficient storage of traceability data emerges as a formidable obstacle.

Related work

Prior research used blockchain to address grain-and-oil-food-supply chain challenges, merging with IoT for transparent data collection. However, concerns persist about IoT vulnerabilities. Recent innovations include lightweight crop storage, dynamic quality systems, and multichain traceability. Blockchain-machine learning integration ensures authenticity, and new storage and recovery methods bolster supply-chain resilience, safeguarding China's grain and oil supply chain.

Proposed method

The foundational structure of blockchain, composed of timestamped chain blocks always offers robust traceability features. Components such as distributed storage, smart contracts, and consensus mechanisms elevate traceability in the grain-and-oil-food supply chain. To overcome blockchain's storage challenges, solutions like sharding, multi-chain architectures, and integration with IPFS are employed. Smart contracts execute predefined terms automatically, while consensus mechanisms maintain system integrity. Privacy enhancements encompass identity protection and data safeguarding using encryption and mixed currency technology.

The fusion of blockchain and IoT enhances traceability efficiency, with EOSIO 2.0 acting as a pivotal open-source blockchain software that synergizes well with IoT. Machine learning contributes to traceability through an outlier-detection model, combining isolated forest, random forest, and logistic regression algorithms for accurate and efficient anomaly identification, thereby bolstering traceability systems.

Experimental results and analysis

The grain-and-oil-food-supply chain operates through distinct stages: production, processing, logistics, warehousing, and sales. Activities such as recording cultivation details, processing equipment, and transportation specifics are tracked. The integration of RFID technology facilitates data capture, with traceability information categorized as public and private data to ensure both sharing and privacy.

This present work relies on Hyperledger Fabric 1.2.0 and Python 3.8, enhancing scalability and security. RFID-driven data collection feeds into a business layer where an outlier-detection model verifies information, leveraging blockchain for transparent and tamper-proof data storage. SM3 encryption and IPFS are integrated into the storage layer, securing data while facilitating recovery. The model's performance highlights its accuracy and recall, achieving 96% accuracy, 98% recall, and high F1 scores. Data queries are expedited using mapping relationships and IPFS, with recovery mechanisms achieving an average latency of 1.2 s. Overall, this work tackles challenges in the grain-and-oil-food-supply chain, focusing on authenticity, storage optimization, and recovery mechanisms.

Future Work

Future work in this field involves an opportunity to analyze deeper into the integration of the grain-and-oil-food blockchain with emerging technologies like the IoT and AI. The complexity of the grain-and-oil-food-supply chain warrants continued research to address challenges and maximize the potential benefits. By harnessing the capabilities of IoT devices, real-time data collection and analysis could be further refined, enhancing the accuracy and timeliness of traceability information. Additionally, the application of AI algorithms could offer advanced insights, predictive analytics, and anomaly detection, elevating the overall efficiency and effectiveness of the traceability system. As technology continues to evolve, the collaboration between blockchain, IoT, and AI has the potential to revolutionize the grain-and-oil-food-supply chain, ensuring both safety and transparency for consumers.

Conclusion

In conclusion, this study has presented a robust and innovative approach to enhancing traceability within the grain- and oil food supply chain by integrating blockchain technology. The proposed model not only ensures data accuracy and reliability through an outlier-detection mechanism but also addresses storage challenges through the "blockchain + database + IPFS" storage approach.

The demonstrated system uses Hyperledger Fabric to showcase its practicality and effectiveness in achieving multi-source data uploading, lightweight storage, and efficient data recovery. By adopting this model, the traceability data within the grain-and-oil-food-supply chain can be rendered more authentic and trustworthy. While challenges persist, the success of this endeavor paves the way for future explorations, particularly in integrating blockchain with IoT and AI, offering new dimensions of safety and innovation in the grain and oil food industry.