Blockchain-Based Supply Chain Transparency for Agricultural Produce

Abstract

Transparency and fairness are tough to guarantee in agricultural supply chains, especially for products that don\'t have clear pricing standards like Maximum Retail Price (MRP). Without traceability, middlemen can step in and push prices up or down as they see fit, making it hard for consumers and everyone involved to trust the process. To tackle this, we introduce a blockchain-based system that brings real transparency to the supply chain. The idea is simple: track every step—from the farmer\'s field to the customer\'s basket—on a decentralised and tamper-proof blockchain. Each phase—farming, processing, distribution, retail—creates an unchangeable record, ensuring data stays secure and trustworthy.But transparency alone isn\'t enough. That\'s why the system also includes a Deep Reinforcement Learning-based Supply Chain Management (DR-SCM) model. It uses past transaction data to predict how demand will shift and helps manage decisions about production, storage, and getting goods where they need to go. This means fewer resources wasted, higher profits for stakeholders, and products reach buyers more reliably. Security matters, too: the system uses SHA-2 to create unique block hashes and AES to protect transaction data, keeping everything encrypted and confidential on the blockchain servers.The platform isn\'t just for one group either. Farmers, processors, distributors, retailers, and customers all interact in the same digital space. Customers aren\'t left in the dark—they can check if a product is genuine, see its pricing history, and know exactly where it came from before they buy. This boosts trust and encourages fair trading practices. By ditching traditional centralised systems (which are prone to single points of failure), this solution sets a new standard for transparency and reliability throughout the supply chain. In short, this research brings together blockchain and machine learning to deliver a smarter, safer, and more transparent agricultural supply chain. The result: Increased consumer confidence and sharper efficiency across the agri-food industry.

Introduction

The agricultural supply chain is essential for food distribution and economic stability, but it faces major issues such as lack of transparency, price manipulation, and inefficient resource management. The absence of standardized pricing (like MRP) allows unfair price increases, while traditional systems provide limited visibility into product origin and handling, reducing consumer trust. Existing technologies like RFID, IoT, and NFC improve monitoring but rely on centralized systems, making them vulnerable to data tampering and security risks.

To address these challenges, the proposed system integrates blockchain technology with Deep Reinforcement Learning (DRL) to create a transparent, secure, and intelligent supply chain. Blockchain ensures decentralized, tamper-proof traceability, allowing every transaction—from farmer to consumer—to be recorded and verified. This enhances trust, accountability, and product authenticity.

In addition to traceability, the system incorporates a Deep Reinforcement Learning-based Supply Chain Management (DR-SCM) model, which:

• Predicts demand using historical data
• Optimizes production, storage, and distribution decisions
• Minimizes waste and maximizes profit

The system uses strong security mechanisms such as SHA-256 hashing for data integrity and AES-256 encryption for data confidentiality. It follows a multi-layered architecture, including stakeholder, application, blockchain core, intelligent contract, and security layers.

Key features include:

• End-to-end product traceability across all stakeholders (farmers, processors, distributors, retailers, customers)
• Smart contracts for automated validation and transactions
• Price regulation mechanisms to prevent excessive profit margins
• Customer access to full product history and pricing transparency
• Continuous optimization loop using DRL for better decision-making

The system workflow tracks products from registration by farmers to final purchase by consumers, ensuring complete visibility at every stage.

Conclusion

This study introduces a comprehensive blockchain-powered system for agricultural supply chains, pushing transparency, traceability, and data security to the forefront of agri-food technology. Traditional supply chains have long struggled with hidden pricing mechanisms, data tampering vulnerabilities, and eroding consumer trust—challenges that this system addresses through innovative integration of distributed ledger technology and machine learning optimisation. As agricultural products traverse from farmers\' fields toward retail endpoints, every transaction is immutably recorded on a decentralised blockchain. This cryptographic immutability ensures data integrity, prevents retrospective manipulation, and establishes unprecedented levels of accountability among all supply chain participants. The system\'s cryptographic infrastructure, leveraging SHA-256 hashing and AES-256 encryption, provides military-grade security protecting sensitive transaction data from unauthorized access and tampering attempts. The integration of intelligent pricing control mechanisms significantly reduces unfair markups by intermediaries, promoting equitable profit distribution and enhancing trade fairness. Comparative evaluations demonstrate the system\'s superiority over both traditional centralised and IoT-based supply chain implementations across transparency, traceability, and security dimensions. Consumers benefit from complete visibility into their food\'s provenance journey, enabling informed purchasing decisions and building genuine confidence in product authenticity. The Deep Reinforcement Learning optimisation component adds predictive intelligence to the transparency infrastructure, enabling data-driven decision-making that enhances operational efficiency and profitability. This hybrid approach—combining blockchain\'s trust infrastructure with machine learning\'s analytical capabilities—represents a significant advancement in agricultural supply chain management. The framework presented herein is not merely theoretically sound but practically implementable, laying robust groundwork for safer food systems, reduced fraud incidence, and fairer pricing throughout the agri-food sector. While acknowledging identified limitations, the system is positioned for real-world deployment, with future development trajectories targeting scalability enhancement, government system integration, and expanded machine learning capabilities for widespread industry adoption.

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Copyright

Copyright © 2026 Prof. Vinni S. Wadvani, Miss. Isha A. Dixit, Miss. Esha A. Lonkar, Miss. Priyanka F. Khadke, Miss. Shreya A. Jumle, Miss. Punam A. Adhao, Mr Adesh S. Dhandar. 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.