A Blockchain-Enabled, AI-Augmented Traceability and Sustainability Framework for Agricultural and Agroforestry Supply Chains
ABSTRACT
This dissertation proposes AgriTrace™, an integrated framework combining permissioned blockchain, AI-driven analytics, and digital traceability to enhance transparency, trust, and sustainability in agricultural and agroforestry supply chains. The study designs, implements, and evaluates a scalable system that links farm-level data capture to verifiable market authentication while enabling ESG reporting and carbon credit generation. Using mixed methods (design science + empirical evaluation), the research assesses system performance, data integrity, user adoption, and economic impact.
CHAPTER 1: INTRODUCTION
1.1 Background of the Study
Global agricultural supply chains face increasing demands for traceability, sustainability, and compliance. Fragmented recordkeeping and weak verification mechanisms lead to fraud, inefficiency, and limited market access. Advances in blockchain, AI, and IoT present opportunities to build secure, transparent, and intelligent systems that can transform how agricultural products are tracked and valued.
1.2 Research Problem
Despite technological advances, there is no widely adopted framework that integrates:
- End-to-end traceability
- Tamper-proof verification
- Real-time data capture
- Sustainability and carbon accounting
- Market-facing authentication
1.3 Research Questions
- How can blockchain enhance trust and data integrity in agricultural supply chains?
- What system architecture best supports scalable traceability from farm to market?
- How can AI improve data validation, anomaly detection, and analytics?
- What is the impact of traceability on market value and stakeholder trust?
- How can carbon credit systems be integrated into traceability platforms?
1.4 Objectives
General Objective
To design, develop, and evaluate an integrated traceability and sustainability framework.
Specific Objectives
- Develop blockchain-based traceability architecture
- Implement AI-assisted data validation and analytics
- Integrate QR-based authentication
- Develop ESG and carbon tracking modules
- Evaluate system performance and adoption
1.5 Hypotheses
H1: Blockchain-based systems significantly improve data integrity.
H2: Traceability increases buyer trust and willingness to pay.
H3: AI-assisted validation reduces data anomalies.
1.6 Significance of the Study
- Academic: advances in agri-blockchain systems
- Industry: improved transparency and market access
- Policy: supports regulatory compliance
1.7 Scope and Limitations
Scope includes system design, prototype development, and pilot testing. Limitations include scalability constraints and simulated data environments.
CHAPTER 2: REVIEW OF RELATED LITERATURE
2.1 Blockchain in Supply Chains
Explores decentralized ledgers, consensus mechanisms, and applications in traceability.
2.2 Agricultural Traceability Systems
Discusses existing systems and their limitations.
2.3 Artificial Intelligence in Agriculture
Covers predictive analytics, anomaly detection, and decision support systems.
2.4 Sustainability and Carbon Markets
Examines carbon accounting, ESG frameworks, and credit certification.
2.5 Research Gap
Current systems lack integration of blockchain, AI, and sustainability into a unified platform.
CHAPTER 3: RESEARCH METHODOLOGY
3.1 Research Design
Design Science Research (DSR) combined with quantitative evaluation.
3.2 System Architecture
- User Layer
- Application Layer
- AI Analytics Layer
- Blockchain Layer
- Data Layer
3.3 Development Approach
Agile iterative development with prototyping cycles.
3.4 Data Collection
- Surveys
- Interviews
- System logs
3.5 Evaluation Methods
- Usability testing (SUS)
- Performance benchmarking
- Statistical analysis (t-test, regression)
CHAPTER 4: SYSTEM DESIGN AND IMPLEMENTATION
4.1 System Overview
AgriTrace™ integrates multiple modules into a unified platform.
4.2 Modules
- Registration
- Data Capture
- Batch Processing
- Blockchain Verification
- QR Authentication
- Sustainability Tracking
4.3 AI Components
- Anomaly detection
- Predictive analytics
4.4 Implementation Tools
- Frontend: React
- Backend: Node.js
- Blockchain: Hyperledger/Ethereum
- Database: SQL/NoSQL
CHAPTER 5: RESULTS AND DISCUSSION
5.1 System Testing
Performance, usability, and security results.
5.2 Data Analysis
Statistical validation of hypotheses.
5.3 Discussion
Interpretation of findings and implications.
CHAPTER 6: CONCLUSIONS AND RECOMMENDATIONS
6.1 Conclusions
AgriTrace™ demonstrates the viability of blockchain-based traceability systems.
6.2 Recommendations
- Scale deployment
- Integrate IoT
- Expand carbon credit systems
CONTRIBUTIONS OF THE STUDY
- Novel integrated framework
- AI-enhanced blockchain traceability
- ESG and carbon integration model
TIMELINE (3–4 YEARS)
Year 1: Literature & proposal
Year 2: System development
Year 3: Testing & data collection
Year 4: Writing & defense
REFERENCES
(To be developed in APA/Scopus-indexed sources)