A Blockchain-Integrated and AI-Enhanced Traceability Framework for Ensuring Authenticity in Organic Food Supply Chains

Abstract

The rapid global shift toward organic food consumption has increased the need for reliable methods to verify product authenticity and track supply-chain activities. Conventional record-keeping systems are highly susceptible to manipulation, missing data, and certification fraud, making consumers uncertain about product quality. This research presents an advanced Organic Food Traceability Framework called TrustTrace, designed using blockchain technology, artificial intelligence, and cloud-based data management. Blockchain ensures immutable and transparent event logging, while an AI-based Trust Score evaluates each product batch using parameters such as certification validity, freshness, logistic performance, and stakeholder behavior. MongoDB Atlas functions as the cloud backbone for storing dynamic multi-stakeholder data. Consumers verify authenticity through QR-based access to tamper-proof farm-to-fork histories. This integrated approach significantly improves data reliability, minimizes fraud, and increases consumer confidence in organic food ecosystems.

Introduction

Organic food demand is rising globally, but counterfeit products and mislabeling threaten consumer trust. Traditional supply-chain methods are vulnerable to tampering and inefficiency. This research proposes TrustTrace, a blockchain-enabled traceability system integrated with AI analytics and cloud storage (MongoDB Atlas), to ensure transparency, authenticity, and reliability across the organic food supply chain.

The system tracks products from farm to consumer, with each stakeholder—farmers, manufacturers, distributors, retailers—logging events that are permanently stored on the blockchain. AI evaluates these inputs to generate a Trust Score, providing consumers with instant verification via QR codes. Key principles include transparency, immutability, traceability, accountability, and data-driven validation.

TrustTrace demonstrates multiple benefits: preventing fraud, improving supply-chain efficiency, enhancing consumer confidence, supporting sustainability, and ensuring data security. The prototype shows effective real-time synchronization, tamper detection, and credibility assessment, though limitations include internet dependency, deployment costs, AI accuracy, and integration challenges with external authorities.

Conclusion

This research establishes an effective framework for ensuring authenticity and transparency in organic food supply chains through blockchain and AI. TrustTrace provides secure, trustworthy, and data-driven insights into the farm-to-fork process. Future improvements include IoT integration for real-time monitoring, smart-contract automation, and mobile application development to broaden accessibility.

References

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Copyright

Copyright © 2025 Sanyuj Rathod, Shreya Nagbhidkar, Sanika Parate, Shrushti Babre, Shruti Pannase, Shashank Bhonde. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.