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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM57225
Volume:
10 |
3 |
Mar 2026
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Agricultural subsidy distribution systems in developing economies face persistent challenges including inefficient beneficiary prioritization, manual verification delays, duplicate claims, and lack of transparency. Conventional rule-based allocation frameworks fail to dynamically assess farmer-specific risk factors such as landholding size, crop profile, financial vulnerability, and historical subsidy utilization. These limitations reduce fairness, accountability, and operational efficiency in subsidy disbursement.This paper presents AgriAidBlock, an integrated AI–Blockchain framework designed to enhance accuracy, transparency, and security in agricultural subsidy allocation. The proposed system employs a supervised machine learning model, specifically XGBoost, to predict and prioritize eligible beneficiaries based on multi-dimensional farmer data. To address concerns of data integrity and trust, a blockchain- based smart contract mechanism is implemented to ensure immutable recording, transparent validation, and tamper-resistant fund disbursement.The integration of predictive analytics with decentralized ledger technology enables automated decision-making, minimizes fraudulent claims, reduces administrative delays, and improves traceability of subsidy transactions. The proposed framework demonstrates how AI-driven prioritization combined with blockchain-enabled governance can significantly enhance the reliability and accountability of agricultural welfare programs
KEYWORDS
Publication Date
5/2/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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