ARTIFICIAL INTELLIGENCE TECHNOLOGIES FOR DIGITAL AGRICULTURE: EFFICIENCY AND SUSTAINABILITY
Abstract
Artificial intelligence (AI) has become a transformative driver in the modernization of agricultural systems, enabling smarter decision-making and greater efficiency across the production cycle. Leveraging tools such as machine learning, computer vision, and data analytics, AI applications are increasingly integrated into crop monitoring, irrigation management, disease detection, and yield prediction. These technologies aim to address key challenges in agriculture, including limited resources, environmental variability, and the demand for sustainable production. Drawing on findings from peer-reviewed studies, this paper presents a consolidated analysis of AI applications in agriculture, categorized by function and evaluated for performance and feasibility. The review also addresses barriers to implementation, such as data quality issues, technological adoption rates, and infrastructural limitations. The synthesis highlights the growing need for interdisciplinary collaboration and policy frameworks to support the scalable deployment of AI innovations in agriculture.
References
1. Bannerjee, G., Sarkar, U., Das, S., & Indrajit, P. (2018). Artificial intelligence in agriculture: A literature survey. International Journal of Scientific Research in Computer Science Applications and Management Studies, 7(3), 1–6.
2. Kumar, A., & Singh, R. (2019). Artificial intelligence in irrigation management: A sustainable approach. Journal of Agricultural Engineering, 56(2), 45–54.
3. Elavarasan, D., & Vincent, P. M. D. R. (2020). Crop yield prediction using deep learning techniques—A review. Computers and Electronics in Agriculture, 178, 105–124. [https://doi.org/10.1016/j.compag.2020.105124](https://doi.org/10.1016/j.compag.2020.105124)
4. Patil, V. C., & Kale, S. M. (2016). Precision farming: The future of Indian agriculture. Indian Journal of Agronomy, 61(3), 243–252.
5. Singh, V., & Misra, A. K. (2017). Detection of plant leaf diseases using image segmentation and soft computing techniques. Information Processing in Agriculture, 4(1), 41–49. [https://doi.org/10.1016/j.inpa.2016.10.005](https://doi.org/10.1016/j.inpa.2016.10.005)
6. Xalima, A. (2023). EFFICIENCY OF APPLYING INNOVATIVE TECHNOLOGIES TO THE PLANT DEVELOPMENT. Universum: технические науки, (7-4 (112)), 33-35.
7. Xalima, A. (2024). THE EFFICIENCY OF ARTIFICIAL INTELLIGENCE IN AGRICULTURAL DEVELOPMENT. Universum: технические науки, 10(11 (128)), 40-42.
8. Xalima, A., Soxibjamol, R., E’Zoza, M., & Sabrina, S. (2024). THE ROLE OF DIGITAL TECHNOLOGIES IN INCREASING THE EFFICIENCY OF AGRICULTURAL ENTERPRISES. Universum: технические науки, 9(4 (121)), 11-13.
9. Yunusovna, A. X. (2024). ENVIRONMENTAL COMPLEXITY AND GREEN INNOVATION: THE SIGNIFICANCE OF OPEN INNOVATION. SCIENTIFIC APPROACH TO THE MODERN EDUCATION SYSTEM, 3(26), 332-338.
10. Yunusovna, A. X., & Anvarjon o‘g‘li, M. S. (2025). METHODS OF USING THE VERILOG PROGRAMMING LANGUAGE FOR LOGIC DEVICE PROGRAMMING. Prospects for innovative technologies in science and education, 2(2), 136-139.
