Elfaki et al. (2026) An auto-validation method for a complete IoT pivot irrigation model based on the Penman–Monteith equation

Identification

• Journal: Scientific Reports
• Year: 2026
• Date: 2026-04-02
• Authors: Abdelrahman Osman Elfaki, Saleh Albelwi, Abderrahim Lakhouit, Osama Moh’d Alia, Mohamed B. D. Elsawy, Anas Bushnag, Raghad Mahmoud Alqobali, Majed A. Alotaibi, Ashraf Marei, Tareq Alhmiedat
• DOI: 10.1038/s41598-026-46804-3

Research Groups

• Department of Computer Science, Faculty of Computers and Information Technology, University of Tabuk, Tabuk, Saudi Arabia
• Department of Civil Engineering, Faculty of Engineering, Geotechnical and Foundations Engineering at University of Tabuk, Tabuk, Saudi Arabia
• Department of Computer Engineering, Faculty of Computers and Information Technology, University of Tabuk, Tabuk, Saudi Arabia
• National Center for Artificial Intelligence (NCAI), Riyadh, Saudi Arabia
• Department of Information Technology, Faculty of Computers and Information Technology, University of Tabuk, Tabuk, Saudi Arabia
• Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, Tabuk, Saudi Arabia

Short Summary

This paper develops and validates an Internet of Things (IoT) pivot irrigation model based on the Penman–Monteith equation, incorporating an auto-validation method to mitigate sensor errors. The proposed system demonstrates significant improvements in optimizing water usage and enhancing agricultural productivity compared to traditional irrigation methods.

Objective

• To develop and validate a comprehensive IoT pivot irrigation model based on the Penman–Monteith equation, incorporating an auto-validation method to protect against sensor errors caused by network interference or natural factors.
• To address key challenges in pivot irrigation systems, including crop water requirements, IoT architecture, data interpretation, environmental factors, scalability, and result validation.

Study Configuration

• Spatial Scale: A single field site with one crop type (grass).
• Temporal Scale: 49 days.

Methodology and Data

• Models used: An IoT pivot irrigation model based on the Penman–Monteith equation.
• Data sources: Real-world experimental sensor data collected from the field site, publicly available on Kaggle.

Main Results

• The proposed IoT pivot irrigation model includes an auto-validation method that effectively protects the system from sensor errors.
• The model comprehensively addresses various challenges in pivot irrigation, making it a robust IoT solution.
• Real-world experiments demonstrated significant improvements in optimizing water usage and enhancing agricultural productivity compared to traditional irrigation methods.
• A benchmark framework for evaluating pivot irrigation systems was developed.
• The study served as a proof-of-concept validation using grass as the crop type.

Contributions

• Introduction of the first comprehensive IoT solution for pivot irrigation that addresses multiple system challenges (crop water requirements, IoT architecture, compatibility, data interpretation, environmental factors, scalability, and result validation).
• Development of an auto-validation method crucial for protecting pivot irrigation systems from sensor errors due to external interferences.
• Establishment of a benchmark framework for standardized performance assessment of pivot irrigation systems in future research.
• Empirical evidence demonstrating the system's effectiveness in optimizing water usage and enhancing agricultural productivity.

Funding

• Deanship of Research and Graduate Studies at the University of Tabuk (Research No. 0113-1444-S).

Citation

@article{Elfaki2026autovalidation,
  author = {Elfaki, Abdelrahman Osman and Albelwi, Saleh and Lakhouit, Abderrahim and Alia, Osama Moh’d and Elsawy, Mohamed B. D. and Bushnag, Anas and Alqobali, Raghad Mahmoud and Alotaibi, Majed A. and Marei, Ashraf and Alhmiedat, Tareq},
  title = {An auto-validation method for a complete IoT pivot irrigation model based on the Penman–Monteith equation},
  journal = {Scientific Reports},
  year = {2026},
  doi = {10.1038/s41598-026-46804-3},
  url = {https://doi.org/10.1038/s41598-026-46804-3}
}