SMART MANAGEMENT OF VERTICAL IRRIGATION SYSTEMS USING AI-POWERED INTERNET OF THINGS (IOT) DEVICES

Authors

  • Akrom Hamiyev Author
  • Kholbek Kholiyorov Author

Keywords:

Artificial Intelligence, Internet of Things, Vertical Irrigation, Smart Agriculture, Water Efficiency, Machine Learning.

Abstract

The integration of Artificial Intelligence (AI) and Internet of Things 
(IoT) technologies has revolutionized agricultural practices, particularly in vertical 
irrigation systems. This paper explores the application of AI-powered IoT devices to 
optimize water usage, enhance crop yield, and reduce operational costs in vertical 
farming. By leveraging real-time data analytics, machine learning algorithms, and 
automated control systems, these technologies enable precise irrigation management. 
The study presents a methodology for implementing such systems, analyzes their 
performance through empirical data, and discusses their implications for sustainable 
agriculture. Results indicate significant improvements in water efficiency and crop 
productivity, with potential scalability across diverse farming environments.

References

1. Smith, J., & Lee, K. (2023). IoT in Agriculture: A Review. Journal of Agricultural

Technology, 12(3), 45-60.

2. Kumar, R., et al. (2022). Real-Time Monitoring of Soil Parameters Using IoT

Sensors. Precision Agriculture, 15(4), 112-125.

3. Zhang, L., & Wang, H. (2024). Machine Learning for Irrigation Optimization. AI in

Agriculture, 8(1), 20-35.

4. Patel, S., & Gupta, A. (2021). Synergy of AI and IoT in Smart Farming. Journal of

Sustainable Agriculture, 10(2), 78-90.

5. Brown, T., et al. (2023). Challenges in Vertical Farming Irrigation. Urban

Agriculture Review, 6(1), 15-28.

6. Li, M., & Chen, Y. (2022). Predictive Analytics in Resource-Constrained

Environments. IoT Journal, 9(5), 200-215.

Published

2025-06-25

How to Cite

SMART MANAGEMENT OF VERTICAL IRRIGATION SYSTEMS USING AI-POWERED INTERNET OF THINGS (IOT) DEVICES . (2025). ОБРАЗОВАНИЕ НАУКА И ИННОВАЦИОННЫЕ ИДЕИ В МИРЕ, 72(1), 317-324. https://scientific-jl.com/obr/article/view/22982