Authors : Dr. K. Santhosh Kumar

Volume/Issue : Volume 11 - 2026, Issue 1 - January

Google Scholar : https://tinyurl.com/5n7v3n2f

Scribd : https://tinyurl.com/muekudva

DOI : https://doi.org/10.38124/ijisrt/26jan1038

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : In contemporary farming, guaranteeing product quality and authenticity is essential for sustaining customer confidence and optimizing value. The combination of block chain and AI promotes sustainable farming by providing transparent data systems and enhancing decision-making, tackling issues like food security, resource waste, climate change, and supply chain transparency. Block chain enhances agriculture with traceable food systems, smart contracts, and carbon credit management. AI aids crop predictions and sustainable methods, while challenges include costs, scalability, and unclear regulations. Affordable and scalable solutions are essential, utilizing standardized data-sharing protocols and clear policies. This review examines technologies for sustainable agriculture, highlighting food safety, carbon credit tracking, climate-smart farming, and supply chain efficiency. It discusses challenges like cost and policy frameworks while proposing future research on system compatibility, real-time analytics, and incentives. Collaboration is essential for agricultural resilience and sustainability.

Keywords : Block chain technology, AI, Green Agriculture, Targeted Farming, Accountability, Automated Contracts, Emission Credit Oversight, Climate-Adaptable Agriculture.

References :

1. Rabah, K., & Singh, M. (2021). Blockchain and artificial intelligence for sustainable agriculture: A systematic review. Journal of Agricultural Informatics. https://doi.org/10.1016/j.aginfo.2021.12.003
2. Chen, Z., & Ahmed, S. (2022). AI-driven decision support systems in smart farming: Blockchain for data security. Computers and Electronics in Agriculture, 198, 107875. https://doi.org/10.1016/j.compag.2022.107875
3. Jiang, X., & Li, Q. (2023). Blockchain technology for agricultural supply chain management: Benefits, challenges, and opportunities. Agricultural Systems, 210, 103612. https://doi.org/10.1016/j.agsy.2023.103612
4. Martínez, P., & Kumar, V. (2022). Integrating AI and blockchain for climate-smart agriculture.
5. Sustainability, 14(12), 8652. https://doi.org/10.3390/su14128652
6. Patel, S., & Verma, R. (2021). Leveraging blockchain and IoT for sustainable agricultural practices.
7. IEEE Access, 9, 3112376. https://doi.org/10.1109/ACCESS.2021.3112376
8. Goyal, A., & Zhang, L. (2020). AI and blockchain in food safety: Enhancing transparency and traceability. Food Control, 120, 107512. https://doi.org/10.1016/j.foodcont.2020.107512
9. Yadav, M., & Sharma, P. (2023). Precision agriculture using blockchain and AI: A survey of recent advances. Journal of Precision Agriculture, 50, 105768. https://doi.org/10.1016/j.preagri.2023.105768
10. IBM Food Trust. (2022). Enhancing food safety with blockchain.
11. Sharma, C., Batra, I., Sharma, S., Malik, A., Hosen, A. S. M. S., & Ra, I.-H. (2022). Predicting trends and research patterns of smart cities: A semi-automatic review using latent Dirichlet allocation (LDA). IEEE Access.
12. https://doi.org/10.1109/ACCESS.2022.3214310
13. Ferguson, R. B., Shapiro, C. A., Hergert, G. W., Kranz, W. L., Klocke, N. L., & Krull, D. H. (1991). Nitrogen and irrigation management practices to minimize nitrate leaching from irrigated corn. Journal of Production Agriculture, 4(2), 186. https://doi.org/10.2134/jpa1991.0186
14. Yadav, V. S., & Singh, A. R. (2019). A systematic literature review of blockchain technology in agriculture. Proceedings of the International Conference on Industrial Engineering and Operations Management, Pilsen, Czech Republic, July 23–26.
15. Xiong, H., Dalhaus, T., Wang, P., & Huang, J. (2020). Blockchain technology for agriculture: Applications and rationale. Frontiers in Blockchain, 3. https://doi.org/10.3389/fbloc.2020.00007
16. Kumar, M., Sandeep, V., Maheshwari, J., Prabhu, V., & Mani, P. (2021). Applying blockchain in agriculture: A study on blockchain technology, benefits, and challenges. In Proceedings of the International Conference on Industrial Engineering and Operations Management. https://doi.org/10.1007/978-3-030-60265-9_11
17. Tian, F. (2016). An agri-food supply chain traceability system for China based on RFID and blockchain technology. In Proceedings of the 13th International Conference on Service Systems and Service Management (ICSSSM), 1–6.
18. Chinaka, M. (2016). Blockchain technology applications in improving financial inclusion in developing economies: Case study for small-scale agriculture in Africa.
19. Costa, C., Antonucci, F., Pallottino, F., Aguzzi, J., Sarriá, D., &Menesatti, P. (2012). A review on agri- food supply chain traceability by means of RFID technology. Food and Bioprocess Technology, 6, 353–366. https://doi.org/10.1007/s11947-012-0836-4
20. Feng, T. (2016). An agri-food supply chain traceability system for China based on RFID & blockchain technology. In Proceedings of the 13th International Conference on Service Systems and Service Management (ICSSSM), Kunming, China, 1–6.
21. Shi, X., An, X., Zhao, Q., Liu, H., Xia, L., Sun, X., & Guo, Y. (2019). State-of-the-art internet of things in protected agriculture. Sensors, 19(1833). https://doi.org/10.3390/s19081833
22. Pang, S., Teng, S. W., Murshed, M., Bui, C. V., Karmakar, P., Li, Y., & Lin, H. (2024). A survey on evaluation of blockchain-based agricultural traceability. Computers and Electronics in Agriculture, 227, 109548. https://doi.org/10.1016/j.compag.2024.109548.
23. Menon, S., & Jain, K. (2024). Blockchain technology for transparency in agri-food supply chain: Use cases, limitations, and future directions. IEEE Transactions on Engineering Management, 71, 106–120. https://doi.org/10.1109/TEM.2021.3110903
24. Puthenveettil, N. R., &Sappati, P. K. (2024). A review of smart contract adoption in agriculture and food industry. Computers and Electronics in Agriculture, 223, 109061. https://doi.org/10.1016/j.compag.2024.109061
25. Porter, G., & Phillips-Howard, K. (1997). Comparing contracts: An evaluation of contract farming schemes in Africa. World Development, 25(2), 227–238. https://doi.org/10.1016/S0305- 750X(96)00101-5.
26. Palani, H. K., Ilangovan, S., Senthilvel, P. G., Thirupurasundari, D. R., & K, R. K. (2023). AI-powered predictive analysis for pest and disease forecasting in crops. In Proceedings of the 2023 International Conference on Communication, Security and Artificial Intelligence (ICCSAI), Greater Noida, India, 950–954. https://doi.org/10.1109/ICCSAI59793.2023.10421237
27. Maghdid, S., Askar, S., Khoshaba, F., & Hamad, S. (2024). Deep learning algorithms for IoT-based crop yield optimization. Indonesian Journal of Computer Science, 13(2). https://doi.org/10.33022/ijcs.v13i2.3846
28. Mahalle, A., & Dongre, S. (2024). Agricultural resource management using technologies like AI, IoT, and blockchain. In The Future of Agriculture: IoT, AI, and Blockchain Technology for Sustainable Farming (pp. 21). https://doi.org/10.2174/9789815274349124010005
29. AlBalooshi, A. S. (2024). Revolutionizing agriculture: Harnessing AI, blockchain, and data science. Source:https://www.linkedin.com/pulse/article-9-revolutionizing-agriculture-harnessing-ai-data- albalooshi - 3bsvf