AI-DRIVEN DIGITAL TRANSFORMATION IN AGRICULTURE: OPPORTUNITIES, CHALLENGES AND FUTURE DIRECTIONS
DOI:
https://doi.org/10.1956/jge.v22i3.897Keywords:
Artificial Intelligence (AI), Precision AgricultureAbstract
The rapid development of artificial intelligence (AI) and digital technologies is driving the transformation in the world of modern agriculture as the new era of data-driven and smart agriculture is emerging. The current review is a compilation of recent papers on AI-driven digital transformation of agriculture and food industries with specific focus on technologies, such as machine learning (ML), deep learning (DL), computer vision, Internet of Things (IoT), big data analytics, and more. The technologies allow real-time monitoring, predictive modelling and intelligent decision support systems to predict crop productivity, soil health, diseases and pests and resource management. In addition, using robotics, autonomous systems and unmanned aerial vehicles (UAVs) is helpful to enhance the efficiency and reduce human involvement. While such initiatives have taken place, there remains a number of challenges such as lack of uniformity and interoperability, cost of investment, cybersecurity and the digital divide, particularly in the developing world. The review also highlights the importance of explainable AI (XAI) solutions, cloud computing and edge computing paradigms in promoting transparency, scalability and acceptance of smart farming tools. Furthermore, emerging technologies (blockchain and digital twins) are explored for improving supply chain transparency and resiliency. The research paper concludes with the need to identify future research opportunities, including the need to build sustainable, scalable and inclusive AI-powered agricultural ecosystems to comply with climate-smart agriculture and global food-security goals.
References
1. Pervez, A. K. M. K., Kabir, M. S., Prodhan, F. A., Rahman, M. H., & Roy, A. (2026). Mapping recent trends in artificial intelligence research for sustainable agriculture: A bibliometric and systematic review. Discover Artificial Intelligence.
https://doi.org/10.1007/s44163-026-01024-6
2. Rajbongshi, A., Johora, F. T., Hossain, A., et al. (2026). Leveraging explainable AI for sustainable agriculture: A comprehensive review of recent advances. Artificial Intelligence Review. https://doi.org/10.1007/s10462-025-11459-5
3. Belattar, S., Abdoun, O., & Haimoudi, E. K. (2026). Economic impact of artificial intelligence on agricultural productivity: A systematic review. Discover Agriculture.
4. Casini, S., Ducange, P., Marcelloni, F., & Pollini, L. (2026). Artificial intelligence in agri-robotics: A systematic review of trends and emerging directions. Robotics, 15(1), 24. https://doi.org/10.3390/robotics15010024
5. Bhat, I. A., Ansarullah, S. I., Ahmad, F., et al. (2025). Leveraging artificial intelligence in agribusiness: A structured review of strategic management practices and future prospects. Discover Sustainability. https://doi.org/10.1007/s43621-025-01260-3
6. Zhang, R., Wu, X., Li, J., et al. (2025). A bibliometric review of deep learning in crop monitoring: Trends, challenges, and future perspectives. Frontiers in Artificial Intelligence. https://doi.org/10.3389/frai.2025.1636898
7. Screpnik, C. R., Zamudio, E., & Gimenez, L. (2025). Artificial intelligence in agriculture: A systematic review of crop yield prediction and optimization. IEEE Access.
8. Aijaz, N., Lan, H., Raza, T., et al. (2025). Artificial intelligence in agriculture: Advancing crop productivity and sustainability. Journal of Agricultural and Food Research.
9. Sood, A., Sharma, R. K., & Bhardwaj, A. K. (2025). Artificial intelligence research in agriculture: A review. Online Information Review.
10. Elbasi, E., Mostafa, N., AlArnaout, Z., et al. (2025). Artificial intelligence technology in the agricultural sector: A systematic literature review. IEEE Access.
11. Kisliuk, B., Krause, J. C., Meemken, H., et al. (2024). AI in current and future agriculture: An introductory overview. KI - Künstliche Intelligenz, 37, 117–132. https://doi.org/10.1007/s13218-023-00826-5
12. Henrietta, M. H. (2024). Artificial intelligence in agriculture: A review of current applications and future trends. IIP Series.
13. Adewusi, A. O., Asuzu, O. F., Olorunsogo, T., et al. (2024). AI in precision agriculture: A review of technologies for sustainable farming practices. World Journal of Advanced Research and Reviews.
14. Aroba, O. J., & Rudolph, M. (2024). Precision agriculture using AI among small-scale farmers: A systematic review. Journal of Infrastructure Policy and Development.
15. Zhou, X., Chen, T., & Zhang, B. (2023). Digital agriculture and green productivity: A review. Land, 12(1), 195.
16. Ganeshkumar, C., Jena, S. K., Sivakumar, A., & Nambirajan, T. (2023). Artificial intelligence in agricultural value chain: Review and future directions. Journal of Agribusiness in Developing and Emerging Economies.
17. Spanaki, K., Sivarajah, U., Fakhimi, M., et al. (2022). Disruptive technologies in agricultural operations: A systematic review. Annals of Operations Research.
18. Chen, T., Lv, L., Wang, D., et al. (2023). Empowering agrifood systems with artificial intelligence: A survey. arXiv.
19. Nawaz, U., Zaheer, M. Z., Khan, F. S., et al. (2025). AI in agriculture: A survey of deep learning techniques. arXiv.
20. Hasan, M. M., Islam, M. U., & Sadeq, M. J. (2022). AI, IoT, and robotics in smart farming: A comprehensive overview. arXiv.
21. Chowdhury, H., Argha, D. B. P., & Ahmed, M. A. (2023). Artificial intelligence in sustainable vertical farming. arXiv.
22. Vishnu, K., Surya, K. R., Kalaivani, A., et al. (2025). AI in agriculture: A comprehensive analysis of recent changes. Journal of Neonatal Surgery.
23. ScienceDirect Editorial Board. (2025). Revolutionizing agriculture using artificial intelligence: A review. Food Chemistry Advances.
24. ScienceDirect Editorial Board. (2025). Enhancing agricultural intelligence with large language models. Artificial Intelligence in Agriculture.
25. Sharma, S., Verma, K., & Hardaha, P. (2023). Implementation of AI in agriculture. Journal of Computational Cognitive Engineering.
26. Songol, M., Awuor, F., & Maake, B. (2021). AI adoption in developing nations agriculture. Journal of Language Technology & Entrepreneurship in Africa.
27. Popa, C. (2021). Adoption of artificial intelligence in agriculture. Bulletin of University of Agricultural Sciences.
28. Rolandi, S., Brunori, G., Bacco, M., & Scotti, I. (2021). Digitalization of agriculture and rural areas. Sustainability.
29. Kamilaris, A., & Prenafeta-Boldú, F. X. (2022). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture.
30. Liakos, K. G., Busato, P., Moshou, D., et al. (2022). Machine learning in agriculture: A review. Sensors.
31. Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2022). Big data in smart farming: A review. Agricultural Systems.
32. Klerkx, L., Jakku, E., & Labarthe, P. (2022). Digital agriculture and innovation systems. NJAS – Wageningen Journal.
33. Lowenberg-DeBoer, J., & Erickson, B. (2022). Precision agriculture adoption trends. Precision Agriculture.
34. Pathak, H. S., Brown, P., & Best, T. (2022). AI for smart agriculture: Review. Agronomy Journal.
35. Talaviya, T., Shah, D., Patel, N., et al. (2022). IoT-based smart agriculture. Procedia Computer Science.
36. Chlingaryan, A., Sukkarieh, S., & Whelan, B. (2022). Machine learning approaches in crop yield prediction. Agricultural Systems.
37. Benos, L., Tagarakis, A., Dolias, G., et al. (2022). Machine learning in agriculture: A review. Computers and Electronics in Agriculture.
38. Zhai, Z., Martínez, J. F., Beltran, V., & Martínez, N. L. (2022). Decision support systems in agriculture. Computers and Electronics in Agriculture.
39. Verdouw, C., Tekinerdogan, B., Beulens, A., & Wolfert, S. (2022). Digital twins in agriculture. Agricultural Systems.
40. Ayaz, M., Ammad-Uddin, M., Sharif, Z., et al. (2022). Internet of Things in agriculture: A review. Future Generation Computer Systems.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Global Economy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
