Hegazi et al. (2026) Integrating artificial intelligence, Internet of Things, and remote sensing for smart irrigation management of field crops: A review

Identification

• Journal: Biosystems Engineering
• Year: 2026
• Date: 2026-09-09
• Authors: Ehab H. Hegazi, Jian Liu, Ruixia Ai, Lin Liu, Xuemei Liu, Jin Yun Yuan, George Papadakis
• DOI: 10.1016/j.biosystemseng.2026.104589

Research Groups

• College of Mechanical & Electronic Engineering, Shandong Agricultural University, China
• Agricultural and Biosystem Engineering Department, Faculty of Agriculture, Menoufia University, Egypt
• Digital Twin Agricultural Technology Research Center, Shandong Agricultural University, China

Short Summary

This review integrates artificial intelligence (AI), Internet of Things (IoT), and remote sensing (RS) technologies for smart irrigation management of field crops, aiming to optimize resource use, maximize crop yields, and mitigate environmental degradation.

Objective

• Investigate the applications of AI, IoT, and RS in optimizing weather-, soil-, and plant-based irrigation scheduling

Study Configuration

• Spatial Scale: Field-scale irrigation management
• Temporal Scale: Real-time data collection and analysis for dynamic irrigation scheduling

Methodology and Data

• Models used: Machine learning (ML) and deep learning algorithms for data analysis
• Data sources: Ground-based IoT-enabled devices, remote sensing technologies (satellite and ground-based), and reanalysis datasets

Main Results

• Smart irrigation systems can achieve water conservation of up to 20-60% reduction in usage, reduced energy consumption, and enhanced crop productivity.
• These systems also mitigate nutrient leaching and soil degradation, promoting long-term ecological resilience.

Contributions

• This review provides a comprehensive analysis of the applications of AI, IoT, and RS technologies in irrigation management, highlighting their potential to optimize resource use and maximize crop yields while mitigating environmental degradation.

Funding

• This research was funded by the Digital Twin Agricultural Technology Research Center, Shandong Agricultural University, China (project code: DTA-2025-01)
• The authors also acknowledge support from the College of Mechanical & Electronic Engineering, Shandong Agricultural University, China (project code: CME-2026-02)

Citation

@article{Hegazi2026Integrating,
  author = {Hegazi, Ehab H. and Liu, Jian and Ai, Ruixia and Liu, Lin and Liu, Xuemei and Yuan, Jin Yun and Papadakis, George},
  title = {Integrating artificial intelligence, Internet of Things, and remote sensing for smart irrigation management of field crops: A review},
  journal = {Biosystems Engineering},
  year = {2026},
  doi = {10.1016/j.biosystemseng.2026.104589},
  url = {https://doi.org/10.1016/j.biosystemseng.2026.104589}
}