Real-Time Smart Farming with Ai Prediction and Blockchain-Based Fair Trade Mechanism
Authors
Quaid-E-Millath Government College for Women, Chennai (India)
Quaid-E-Millath Government College for Women, Chennai (India)
Article Information
DOI: 10.51584/IJRIAS.2026.110200094
Subject Category: Artificial Intelligence
Volume/Issue: 11/2 | Page No: 1077-1083
Publication Timeline
Submitted: 2026-02-20
Accepted: 2026-02-25
Published: 2026-03-14
Abstract
The increasing demand for data-driven and transparent agricultural systems has led to the adoption of advanced digital technologies. This paper presents the second phase implementation of a real-time smart farming platform that integrates Internet of Things (IoT), Artificial Intelligence (AI), and blockchain technologies. IoT sensors continuously monitor field conditions and transmit real-time data to a backend server for processing and storage. An AI-based prediction module analyzes sensor data to support timely agricultural decision-making. To ensure fair and transparent trade, blockchain-based smart contracts are employed to record and execute agricultural transactions without intermediaries. Experimental results demonstrate reliable real-time data handling, effective AI prediction performance, and secure trade execution, validating the practicality of the implemented system.
Keywords
Smart Farming, Internet of Things, Artificial Intelligence
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References
1. Rustemi and F. Dalipi, “Synergizing IoT, AI, and blockchain for smart agriculture: Challenges, opportunities, and future directions,” *Comput. Electr. Eng.*, 2025, doi:10.1016/j.compeleceng.2025.101778. :contentReference[oaicite:0]{index=0} [Google Scholar] [Crossref]
2. T. Miller, “The IoT and AI in agriculture: The time is now—A systematic review,” *Sensors*, vol. 25, no. 12, 2025. :contentReference[oaicite:1]{index=1} [Google Scholar] [Crossref]
3. S. Safeer et al., “IoT based climate smart agriculture succeeded by blockchain integration for sustainability,” *Sustain. Food Syst.*, 2024. :contentReference[oaicite:2]{index=2} [Google Scholar] [Crossref]
4. V. Choudhary et al., “An overview of smart agriculture using internet of things,” *Comput. Electron. Agric.*, 2025. :contentReference[oaicite:3]{index=3} [Google Scholar] [Crossref]
5. M. Nawaz and M. I. Khan Babar, “IoT and AI for smart agriculture in resource-constrained environments: Challenges, opportunities and solutions,” *Comput. Electr. Eng.*, 2025. :contentReference[oaicite:4]{index=4} [Google Scholar] [Crossref]
6. O. Elijah, T. A. Rahman, I. Orikumhi, C. Y. Leow, and M. N. Hindia, “An overview of internet of things (IoT) and data analytics in agriculture: Benefits and challenges,” *IEEE Internet Things J.*, vol. 5, no. 5, pp. 3758–3773, Oct. 2018. :contentReference[oaicite:5]{index=5} [Google Scholar] [Crossref]
7. M. Ferrag, L. Shu, X. Yang, A. Derhab, and L. Maglaras, “Security and privacy for green IoT-based agriculture: Review, blockchain solutions, and challenges,” *IEEE Access*, vol. 8, pp. 32031–32053, Feb. 2020. :contentReference[oaicite:6]{index=6} [Google Scholar] [Crossref]
8. O. Friha, M. A. Ferrag, L. Shu, L. Maglaras, and X. Wang, “Internet of Things for the future of smart agriculture: A comprehensive survey of emerging technologies,” *IEEE/CAA J. Automatica Sinica*, vol. 8, no. 4, pp. 718–752, Apr. 2021. :contentReference[oaicite:7]{index=7} [Google Scholar] [Crossref]
9. M. S. M. Rafi, M. Behjati, and A. S. Rafsanjani, “Reliable and cost-efficient IoT connectivity for smart agriculture: A comparative study of LPWAN, 5G, and hybrid models,” *IEEE Internet Things J.*, vol. 12, no. 3, pp. 1578–1590, Mar. 2025. :contentReference[oaicite:8]{index=8} [Google Scholar] [Crossref]
10. X. Li, H. Wang, and J. Chen, “AI and IoT-based crop monitoring system: A case study,” *IEEE Internet Things J.*, vol. 11, no. 3, pp. 2120–2132, Mar. 2024. :contentReference[oaicite:9]{index=9} [Google Scholar] [Crossref]
11. N. S. Sizan, M. A. Layek, and K. F. Hasan, “Secured triad of IoT, machine learning, and blockchain for crop forecasting in agriculture,” *Int. J. Comput. Appl.*, vol. 182, no. 5, pp. 45–56, May 2025. :contentReference[oaicite:10]{index=10} [Google Scholar] [Crossref]
12. M. Mollah, S. Zhao, and K. Islam, “Blockchain-based solutions for agriculture supply chain: Security, transparency, and traceability,” *IEEE Access*, vol. 11, pp. 35467–35480, Apr. 2023. :contentReference[oaicite:11]{index=11} [Google Scholar] [Crossref]
13. P. Ray, “Internet of things for smart agriculture: Technologies, practices and future direction,” *J. Ambient Intell. Smart Environ.*, vol. 9, no. 4, pp. 395–420, Jun. 2017. :contentReference[oaicite:12]{index=12} [Google Scholar] [Crossref]
14. Z. Babar and O. B. Akan, “Sustainable and precision agriculture with the Internet of Everything (IoE),” *Int. J. Agric. Inform.*, vol. 15, no. 2, pp. 102–115, Apr. 2024. :contentReference[oaicite:13]{index=13} [Google Scholar] [Crossref]
15. “Real-time framework for interoperable semantic-driven IoT in smart agriculture,” arXiv preprint, Oct. 2025. :contentReference[oaicite:14]{index=14} [Google Scholar] [Crossref]
16. “LoRa communication for Agriculture 4.0: Opportunities and challenges,” arXiv preprint, Sep. 2024. :contentReference[oaicite:15]{index=15} [Google Scholar] [Crossref]
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