Ghazi (2025) Smart Irrigation Systems: A Comprehensive Review of IoT, AI, and Sustainable Agriculture Technologies. A Review Article

Identification

• Journal: Kirkuk University Journal For Agricultural Sciences
• Year: 2025
• Date: 2025-12-18
• Authors: Ghazi
• DOI: 10.58928/ku25.16429

Research Groups

• Department of Agricultural Machinery and Equipment, College of Agriculture, Kirkuk University, Kirkuk, IRAQ
• College of Medicinal & Industrial Plants, Kirkuk University, IRAQ
• Department of Therapeutic Nutrition Techniques, College of Health and Medical Techniques, Kirkuk, Northern Technical University, IRAQ
• Department of Agricultural Machinery and Technology Engineering, Çukurova University Faculty of Agriculture, Adana, TÜRKİYE

Short Summary

This review synthesizes recent advances in multi-source sensing, IoT/LPWAN connectivity, and hybrid edge–cloud AI frameworks for real-time irrigation and fertigation optimisation. It finds that AI- and sensor-driven scheduling commonly reduces water use by 15–40% while maintaining or improving yield and nutrient-use efficiency across various agricultural systems.

Objective

• To provide a comprehensive and integrated analysis of sensing, communication, edge–cloud orchestration, and AI-driven control in smart irrigation systems.
• To offer a quantitative synthesis of performance outcomes (water savings, yield, nutrient-use efficiency).
• To outline a research roadmap toward interoperable, trustworthy, and scalable smart irrigation ecosystems.

Study Configuration

• Spatial Scale: Open-field, orchard, and greenhouse systems; multi-scale decision making for heterogeneous fields; future focus on field-to-basin scale digital twins.
• Temporal Scale: Review focuses on high-impact contributions from 2017–2025, with a research roadmap for 2025–2030.

Methodology and Data

• Models used: Machine learning models (Random Forest, XGBoost, Long Short-Term Memory (LSTM), Convolutional Neural Network-LSTM, Transformer), control strategies (Model Predictive Control (MPC), Reinforcement Learning (RL), fuzzy logic), ET estimation models (FAO-56 Penman–Monteith, hybrid feature-engineered ML models), state-space models for soil moisture forecasting, digital twins.
• Data sources: A PRISMA-based methodology was applied to over 150 studies from databases including Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, MDPI, Wiley Online Library, and Taylor & Francis Online. Data also includes satellite imagery, weather reanalysis, multi-farm analytics, and real-time sensor streams (soil moisture, microclimate, canopy temperature, flow, pressure).

Main Results

• Smart irrigation systems, driven by AI and sensors, typically reduce agricultural water consumption by 15–40%.
• These systems maintain or improve crop yield and nutrient-use efficiency across open-field, orchard, and greenhouse environments.
• Advanced machine learning models and control strategies significantly enhance evapotranspiration estimation, soil moisture forecasting, anomaly detection, and automated valve control.
• Commercial platforms demonstrate scalable deployment, integrating IoT diagnostics, hydraulic monitoring, and interoperable APIs.
• Key challenges include sensor drift, connectivity limitations, proprietary architectures, and the limited explainability of deep learning models.

Contributions

• Provides an integrated analysis of sensing, communication, edge–cloud orchestration, and AI-driven control, addressing fragmentation in existing literature.
• Offers a quantitative synthesis of performance outcomes, including water savings, yield, and nutrient-use efficiency.
• Incorporates recent advancements in digital twins, explainable AI, and energy-autonomous edge systems into a cohesive review.
• Proposes a comprehensive research roadmap for future developments in smart irrigation, emphasizing interoperability, trustworthy AI, and self-calibrating sensors.

Funding

Not explicitly stated in the provided text.

Citation

@article{Ghazi2025Smart,
  author = {Ghazi},
  title = {Smart Irrigation Systems: A Comprehensive Review of IoT, AI, and Sustainable Agriculture Technologies. A Review Article},
  journal = {Kirkuk University Journal For Agricultural Sciences},
  year = {2025},
  doi = {10.58928/ku25.16429},
  url = {https://doi.org/10.58928/ku25.16429}
}