Mohammed, N., Tahir, H., Othman, S., Hawar, D., Abdulkadir, M., Korkmaz, C. (2025). Smart Irrigation Systems: A Comprehensive Review of IoT, AI, and Sustainable Agriculture Technologies. A Review Article. , 16(4), 266-281. doi: 10.58928/ku25.16429
Naz Mohammed; Hussain Thahir Tahir; Skala Salam Othman; Dalya Aydeen Hawar; Murad Abdullah Abdulkadir; Cem Korkmaz. "Smart Irrigation Systems: A Comprehensive Review of IoT, AI, and Sustainable Agriculture Technologies. A Review Article". , 16, 4, 2025, 266-281. doi: 10.58928/ku25.16429
Mohammed, N., Tahir, H., Othman, S., Hawar, D., Abdulkadir, M., Korkmaz, C. (2025). 'Smart Irrigation Systems: A Comprehensive Review of IoT, AI, and Sustainable Agriculture Technologies. A Review Article', , 16(4), pp. 266-281. doi: 10.58928/ku25.16429
Mohammed, N., Tahir, H., Othman, S., Hawar, D., Abdulkadir, M., Korkmaz, C. Smart Irrigation Systems: A Comprehensive Review of IoT, AI, and Sustainable Agriculture Technologies. A Review Article. , 2025; 16(4): 266-281. doi: 10.58928/ku25.16429

Smart Irrigation Systems: A Comprehensive Review of IoT, AI, and Sustainable Agriculture Technologies. A Review Article

Kirkuk University Journal for Agricultural Sciences (KUJAS)
Volume 16, Issue 4, December 2025, Pages 266-281    PDF (984.09 K)
Document Type: Review Paper
DOI: 10.58928/ku25.16429
Authors
Naz Mohammed1; Hussain Thahir Tahir2; Skala Salam Othman3; Dalya Aydeen Hawar4; Murad Abdullah Abdulkadir5; Cem Korkmaz* 6
1Kirkuk University - college of Agriculture
2Department of Agricultural machinery and Equipment, College of Agriculture, Kirkuk University, Kirkuk, IRAQ.
3Agricultural machinery and Equipment,College of Agriculture, Kirkuk University,IRAQ
4College of Medicinal & Industrial Plants, Kirkuk University, IRAQ
5Department of Therapeutic Nutrition Techniques, College of Health and Medical Techniques, Kirkuk, Northern Technical University, IRAQ.
6Department of Agricultural Machinery and Technology Engineering, Çukurova University Faculty of Agriculture, Adana, TÜRKİYE
Abstract
ABSTRACT
      Smart irrigation systems have become essential for mitigating water scarcity, climate variability, and rising energy costs in modern agriculture. This review synthesizes recent advances in multi-source sensing, IoT/LPWAN connectivity, and hybrid edge–cloud AI frameworks that enable real-time irrigation and fertigation optimisation. A PRISMA-based methodology was applied to over 150 studies, focusing on high-impact contributions from 2017–2025. Results show that AI- and sensor-driven scheduling commonly reduces water use by 15–40% while maintaining or improving yield and nutrient-use efficiency across open-field, orchard, and greenhouse systems. Machine-learning models (RF, XGB, LSTM, CNN–LSTM, Transformer) and control strategies (MPC, RL, fuzzy logic) significantly enhance ET estimation, soil-moisture forecasting, anomaly detection, and automated valve control. Commercial platforms such as Netafim NetBeat®, Rivulis Manna, Jain AquaSphere, Rain Bird IQ4, and Toro IntelliDash demonstrate scalable field deployment, integrating IoT diagnostics, hydraulic monitoring, and interoperable APIs. Key barriers include sensor drift, connectivity limitations, proprietary architectures, and the limited explainability of deep-learning models. Future directions emphasize interoperable data standards, trustworthy and uncertainty-aware AI, self-calibrating sensing systems, high-fidelity digital twins, and energy-autonomous edge hardware. Collectively, these innovations position smart irrigation as a core enabler of climate-resilient, resource-efficient agricultural water management.
Keywords
Smart irrigation; Precision irrigation; IoT and LPWAN connectivity; Model predictive control; LSTM-based forecasting; Fertigation management; Digital twin and explainable AI
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