Towards an Agentic AI-Enabled Blockchain-Based Fish Supply Chain Using Hyperledger Fabric

Illegal, unreported, and unregulated (IUU) fishing activities have become one of the most critical challenges facing the global fish industry, particularly in developing countries, with the economic impact of fish fraud reaching billions of dollars annually. A major contributor to this problem is the limitation of conventional fish supply chain systems, which lack secure data sharing among stakeholders, fail to provide trusted product information to consumers, and offer insufficient transparency for regulatory authorities. These shortcomings facilitate fraud and weaken trust and oversight across the supply chain. Blockchain technology has demonstrated strong capability to address key cybersecurity challenges by enhancing traceability, transparency, and tamper-resistant data integrity across distributed supply chain stakeholders. In this paper, we present an enterprise-oriented prototype of a secure, permissioned blockchain-based fish supply chain system designed to enable trusted data sharing and end-to-end traceability across multi-stakeholder environments. Building upon our prior work in Ethereum-based seafood quality monitoring, this study contributes: (1) a modular, consortium-grade architecture implemented using Hyperledger Fabric and containerized via Docker, supporting scalable organizational participation; (2) formal UML-based system modeling of supply chain actors, assets, and lifecycle transitions; and (3) custom chaincode logic that enforces ownership transfer workflows and regulatory compliance policies. In addition, the architecture is designed as agent-ready, exposing standardized APIs that enable future integration of autonomous AI-driven client applications for proactive supply chain orchestration. By leveraging a private, permissioned network model, the functional prototype demonstrates the feasibility of improving data veracity and providing a practical foundation for mitigating fraud and enhancing regulatory oversight in the global fish industry.

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Publication Details

Journal
Electronics
Published
2026-05-01
DOI
https://doi.org/10.3390/electronics15091916
Primary Topic
Blockchain Technology Applications and Security
Type
article
Field-Weighted Citation Impact
0.00
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article

Towards an Agentic AI-Enabled Blockchain-Based Fish Supply Chain Using Hyperledger Fabric

Eden Teshome Hunde, Bashar Othman, Shereen Ismail, Hassan Reza
Electronics
Blockchain Technology Applications and Security
article

Towards an Agentic AI-Enabled Blockchain-Based Fish Supply Chain Using Hyperledger Fabric

Eden Teshome Hunde, Bashar Othman, Shereen Ismail, Hassan Reza
article en

Abstract

Illegal, unreported, and unregulated (IUU) fishing activities have become one of the most critical challenges facing the global fish industry, particularly in developing countries, with the economic impact of fish fraud reaching billions of dollars annually. A major contributor to this problem is the limitation of conventional fish supply chain systems, which lack secure data sharing among stakeholders, fail to provide trusted product information to consumers, and offer insufficient transparency for regulatory authorities. These shortcomings facilitate fraud and weaken trust and oversight across the supply chain. Blockchain technology has demonstrated strong capability to address key cybersecurity challenges by enhancing traceability, transparency, and tamper-resistant data integrity across distributed supply chain stakeholders. In this paper, we present an enterprise-oriented prototype of a secure, permissioned blockchain-based fish supply chain system designed to enable trusted data sharing and end-to-end traceability across multi-stakeholder environments. Building upon our prior work in Ethereum-based seafood quality monitoring, this study contributes: (1) a modular, consortium-grade architecture implemented using Hyperledger Fabric and containerized via Docker, supporting scalable organizational participation; (2) formal UML-based system modeling of supply chain actors, assets, and lifecycle transitions; and (3) custom chaincode logic that enforces ownership transfer workflows and regulatory compliance policies. In addition, the architecture is designed as agent-ready, exposing standardized APIs that enable future integration of autonomous AI-driven client applications for proactive supply chain orchestration. By leveraging a private, permissioned network model, the functional prototype demonstrates the feasibility of improving data veracity and providing a practical foundation for mitigating fraud and enhancing regulatory oversight in the global fish industry.

ElectronicsVol. 15(9)
Jimma University (ET), University of North Dakota (US), University of Michigan (US), Merit Network (US), Saudi Arabia Basic Industries (Saudi Arabia) (SA)
Openalex Percentile: Top 4%
Blockchain Technology Applications and Security
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