Explainable Artificial Intelligence Integrated with Blockchain for Transparent and Trustworthy Agricultural Supply Chain Systems

Abstract

Agricultural supply chains continue to face persistent challenges, including weak traceability, counterfeit records, quality disputes, fragmented information exchange, and limited stakeholder trust. These issues are especially serious in multi-tier supply networks, where farm data, logistics records, certification documents, and retail claims are often distributed across disconnected systems. Explainable Artificial Intelligence (XAI) and blockchain are two emerging technologies that can address these limitations from complementary perspectives. XAI improves the interpretability of AI-based predictions and recommendations, while blockchain provides immutable, time-stamped, and distributed recordkeeping for supply chain events. This manuscript proposes a conceptual framework that integrates XAI with blockchain to support transparent and trustworthy agricultural supply chain systems. The framework enables provenance verification, automated compliance, fraud detection, quality prediction, and trust-aware decision support. A simulation-based evaluation is presented to show how the proposed architecture can improve traceability time, fraud detection accuracy, explanation usefulness, and stakeholder trust when compared with centralized and single-technology alternatives. The study argues that combining XAI and blockchain can reduce information asymmetry, strengthen accountability, and support a more resilient farm-to-fork ecosystem.

Introduction

Agricultural supply chains increasingly require transparency, traceability, and trust to verify product origin, quality, safety, and sustainability. Traditional paper-based and centralized systems often fail to provide reliable records due to fragmentation, manual updates, and vulnerability to tampering, leading to food fraud, certification disputes, and reduced consumer confidence.

To address these challenges, Blockchain Technology and Explainable Artificial Intelligence (XAI) can be integrated. Blockchain provides secure, decentralized, and tamper-resistant record keeping, ensuring end-to-end traceability across farmers, processors, transporters, warehouses, and retailers. Meanwhile, AI supports tasks such as quality assessment, spoilage prediction, demand forecasting, logistics optimization, and fraud detection. However, conventional AI models often act as "black boxes," making their decisions difficult to understand. XAI solves this problem by providing clear explanations of how and why predictions are made.

Related Work

Previous studies have shown that:

• Blockchain enhances traceability, provenance verification, transparency, and smart-contract automation in agricultural supply chains.
• AI improves agricultural decision-making through crop monitoring, quality grading, spoilage detection, and logistics optimization.
• XAI increases trust by making AI decisions understandable.
• Although blockchain and AI have been studied separately and together in some contexts, comprehensive frameworks combining blockchain-based traceability with XAI-driven decision transparency remain limited.

Research Gap

Current systems face three major limitations:

1. Blockchain-based traceability records events but rarely explains AI-generated decisions.
2. XAI systems provide explanations but do not securely preserve them against tampering.
3. Few solutions integrate traceability, auditability, explainability, and compliance management within a single framework.

Objectives

The study aims to:

• Develop an integrated XAI-blockchain architecture.
• Improve transparency and stakeholder trust through explainable predictions.
• Preserve provenance and decision evidence using blockchain.
• Evaluate the framework's impact on traceability and fraud detection.
• Discuss real-world implementation challenges.

Proposed Framework

The framework consists of five layers:

1. Data Acquisition Layer – Collects data from IoT sensors, farms, weather services, warehouses, logistics systems, and certification databases.
2. Analytics Layer – Uses machine learning for quality assessment, spoilage prediction, fraud detection, and demand forecasting.
3. Explanation Layer – Generates human-understandable explanations for AI decisions.
4. Blockchain Layer – Stores event records, decision explanations, timestamps, digital signatures, and smart-contract outputs.
5. Application Layer – Provides dashboards for farmers, regulators, traders, retailers, exporters, and consumers.

System Workflow

The process includes:

1. Data collection from supply chain participants.
2. Data preprocessing.
3. AI prediction of events such as spoilage, fraud, or quality grades.
4. Generation of explanations through XAI.
5. Validation through smart contracts.
6. Recording of events and explanations on blockchain.
7. Stakeholder access through dashboards.
8. Automated alerts for anomalies or violations.

This ensures decisions are both automated and auditable.

Methodology

A simulation involving 10,000 synthetic supply chain events was conducted. Four systems were compared:

• Centralized database system.
• Blockchain-only system.
• XAI-only system.
• Integrated XAI + Blockchain system.

Evaluation metrics included:

• Traceability verification time.
• Fraud detection accuracy.
• Explanation usefulness.
• Stakeholder trust.
• Tamper resistance.

Results

The integrated XAI-Blockchain system achieved the best performance:

Key findings:

• Blockchain significantly improves traceability and data security.
• XAI enhances interpretability and stakeholder trust.
• Combining both technologies produces the strongest overall performance.

Discussion

The integrated framework creates a robust trust infrastructure by:

• Explaining AI-driven decisions.
• Maintaining immutable records of supply chain events.
• Supporting fair trade and automated payment settlement through smart contracts.
• Reducing disputes and increasing accountability.
• Minimizing information asymmetry among supply chain participants.

Stakeholders can understand product grading decisions, verify product provenance, monitor logistics risks, and access auditable compliance records.

Practical Applications

The framework can be applied to:

• Farm-to-retail produce traceability.
• Cold-chain monitoring.
• Organic and certified product fraud detection.
• Quality and yield prediction.
• Automated payments using smart contracts.
• Consumer-facing provenance verification systems.

Implementation Considerations

Successful deployment requires:

• Permissioned blockchain networks for privacy and efficiency.
• Off-chain storage for large datasets.
• User-friendly explanation mechanisms.
• Integration with ERP systems, mobile apps, IoT devices, and certification platforms.
• Reliable data collection methods since blockchain cannot verify the correctness of initial inputs.

Conclusion

The integration of Explainable Artificial Intelligence with blockchain offers a strong architectural foundation for transparent and trustworthy agricultural supply chain systems. XAI helps stakeholders understand the reasoning behind AI-driven decisions, while blockchain protects the integrity and traceability of supply chain records. The proposed framework demonstrates how these technologies can work together to improve provenance verification, compliance automation, fraud detection, and stakeholder trust. The simulation-based results suggest that the integrated system outperforms centralized, blockchain-only, and XAI-only alternatives. This makes the approach highly relevant for modern agricultural ecosystems that increasingly demand accountability, transparency, and reliability.

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Copyright

Copyright © 2026 Pankaj Kumar, Dr. Rakesh Kumar Verma. 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.