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<p>Agri-Food Supply Chains (AFSCs) play a vital role in ensuring food safety, transparency, and economic sustainability; however, they remain vulnerable to food fraud, poor traceability, data silos, and operational inefficiencies. Although blockchain technologies have been explored to address these challenges, many existing solutions suffer from high computational overhead, energy inefficiency, scalability limitations, and limited support for intelligent fraud detection. To address these issues, this paper proposes the Deep Recurrent Edge-Based enhanced Blockchain Agri-food Traceability (DR2B-AgriT) framework. The framework integrates a decentralized blockchain ledger using Proof-of-Authority (PoA) consensus with a Deep Recurrent Edge Heterogeneous Graph Q Network (DREHGQN) for intelligent product tracking, fraud detection, and adaptive decision-making. Data confidentiality and integrity are ensured through homomorphic encryption with cryptographic keys optimized using iHow optimization algorithm, with digital signatures. In addition, Multi-Factor Authentication (MFA) integrated with self-sovereign identity provides secure and privacy-preserving access control. Experimental results demonstrate that DR2B-AgriT achieves 98.5% track-and-trace accuracy, 856.3 TPS throughput, low response latency, and a storage overhead of only 88.7&#xa0;MB, confirming its suitability for secure, scalable, and efficient AFSC management.</p>

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A deep recurrent edge-based blockchain framework for secure agri-food supply chain traceability

  • 期刊论文

  • 发表日期：

    2026年7月31日

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  • R. Femimol,
  • L. Nalini Joseph

摘要

Agri-Food Supply Chains (AFSCs) play a vital role in ensuring food safety, transparency, and economic sustainability; however, they remain vulnerable to food fraud, poor traceability, data silos, and operational inefficiencies. Although blockchain technologies have been explored to address these challenges, many existing solutions suffer from high computational overhead, energy inefficiency, scalability limitations, and limited support for intelligent fraud detection. To address these issues, this paper proposes the Deep Recurrent Edge-Based enhanced Blockchain Agri-food Traceability (DR2B-AgriT) framework. The framework integrates a decentralized blockchain ledger using Proof-of-Authority (PoA) consensus with a Deep Recurrent Edge Heterogeneous Graph Q Network (DREHGQN) for intelligent product tracking, fraud detection, and adaptive decision-making. Data confidentiality and integrity are ensured through homomorphic encryption with cryptographic keys optimized using iHow optimization algorithm, with digital signatures. In addition, Multi-Factor Authentication (MFA) integrated with self-sovereign identity provides secure and privacy-preserving access control. Experimental results demonstrate that DR2B-AgriT achieves 98.5% track-and-trace accuracy, 856.3 TPS throughput, low response latency, and a storage overhead of only 88.7 MB, confirming its suitability for secure, scalable, and efficient AFSC management.