Abstract :

Secure And Transparent Land Registration Remains A Critical Challenge In Agriculture-driven Economies Due To Centralized Record Management, Manual Verification, And High Susceptibility To Fraud. To Address These Limitations, This Project Presents An Enhanced GREENLAND Framework That Integrates Blockchain Technology With Advanced Artificial Intelligence To Deliver A Secure, Intelligent, And Scalable Land Registration System Tailored For Agriculture Industry 5.0. Unlike Conventional Blockchain-based Land Registries That Store All Transactions Indiscriminately, The Proposed System Introduces An AIdriven Validation Layer That Intelligently Analyzes Land Ownership Data, Transaction Attributes, And Historical Patterns To Distinguish Legitimate Records From Potentially Fraudulent Ones. Only Verified Land Records Are Committed To The Blockchain, Significantly Reducing Computational Overhead And Transaction Costs. Smart Contracts Automate Ownership Registration, Transfer, And Verification Processes, Ensuring Immutability, Transparency, And Trustless Execution. To Improve Scalability And Storage Efficiency, Original Land Documents Are Securely Stored In A Decentralized File System, While Cryptographic Hashes Are Maintained On-chain. Experimental Evaluation Demonstrates Improved Classification Accuracy, Reduced Gas Consumption, Enhanced Transaction Throughput, And Secure Smart Contract Deployment Without Detected Vulnerabilities. The Proposed Framework Not Only Strengthens Land Ownership Security But Also Supports Intelligent Agricultural Governance By Enabling Reliable Digital Land Assets. This System Offers A Future-ready Solution For Sustainable, Fraud-resilient, And Technologydriven Land Administration. Index Terms- Blockchain, Artificial Intelligence, Land Registry System, Smart Contracts, Agriculture Industry 5.0, Fraud Detection, Decentralized Storage, InterPlanetary File System (IPFS), Machine Learning, Ethereum Blockchain

Published:

20-2-2026

Issue:

Vol. 26 No. 2 (2026)

Page Nos:

75-79

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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