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Sensors
Volume 24
Issue 23
10.3390/s24237480
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Review

Internet of Things-Based Automated Solutions Utilizing Machine Learning for Smart and Real-Time Irrigation Management: A Review

by
Bryan Nsoh
1,2,
Abia Katimbo
1,2,*,
Hongzhi Guo
3,
Derek M. Heeren
1,
Hope Njuki Nakabuye
4,
Xin Qiao
1,5,
Yufeng Ge
1,
Daran R. Rudnick
6,
Joshua Wanyama
7,
Erion Bwambale
7 and
Shafik Kiraga
8
1
Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
2
West Central Research, Extension, and Education Center, University of Nebraska-Lincoln, North Platte, NE 69101, USA
3
School of Computing, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
4
Texas A&M AgriLife, 1102 East Drew Street, Lubbock, TX 79403, USA
5
Panhandle Research, Extension, and Education Center, University of Nebraska-Lincoln, Scottsbluff, NE 69361, USA
6
Carl and Melinda Helwig Department of Biological and Agricultural Engineering, Kansas State University, Manhattan, KS 66506, USA
7
Department of Agricultural and Biosystems Engineering, Makerere University, Kampala P.O. Box 7062, Uganda
8
Center for Precision and Automated Agricultural Systems, Irrigated Agriculture Research and Extension Center, Department of Biological Systems Engineering, Washington State University, Prosser, WA 99350, USA
*
Author to whom correspondence should be addressed.
Sensors 2024, 24(23), 7480; https://doi.org/10.3390/s24237480 (registering DOI)
Submission received: 26 August 2024 / Revised: 4 November 2024 / Accepted: 8 November 2024 / Published: 23 November 2024
(This article belongs to the Special Issue Sensor and AI Technologies in Intelligent Agriculture: 2nd Edition)
Review Reports Versions Notes

Abstract

This systematic review critically evaluates the current state and future potential of real-time, end-to-end smart, and automated irrigation management systems, focusing on integrating the Internet of Things (IoTs) and machine learning technologies for enhanced agricultural water use efficiency and crop productivity. In this review, the automation of each component is examined in the irrigation management pipeline from data collection to application while analyzing its effectiveness, efficiency, and integration with various precision agriculture technologies. It also investigates the role of the interoperability, standardization, and cybersecurity of IoT-based automated solutions for irrigation applications. Furthermore, in this review, the existing gaps are identified and solutions are proposed for seamless integration across multiple sensor suites for automated systems, aiming to achieve fully autonomous and scalable irrigation management. The findings highlight the transformative potential of automated irrigation systems to address global food challenges by optimizing water use and maximizing crop yields.
Keywords: precision irrigation; sensor networks; artificial intelligence; precision agriculture; water use efficiency; crop productivity; remote monitoring; smart farming; edge computing; interoperability precision irrigation; sensor networks; artificial intelligence; precision agriculture; water use efficiency; crop productivity; remote monitoring; smart farming; edge computing; interoperability

Share and Cite

MDPI and ACS Style

Nsoh, B.; Katimbo, A.; Guo, H.; Heeren, D.M.; Nakabuye, H.N.; Qiao, X.; Ge, Y.; Rudnick, D.R.; Wanyama, J.; Bwambale, E.; et al. Internet of Things-Based Automated Solutions Utilizing Machine Learning for Smart and Real-Time Irrigation Management: A Review. Sensors 2024, 24, 7480. https://doi.org/10.3390/s24237480

AMA Style

Nsoh B, Katimbo A, Guo H, Heeren DM, Nakabuye HN, Qiao X, Ge Y, Rudnick DR, Wanyama J, Bwambale E, et al. Internet of Things-Based Automated Solutions Utilizing Machine Learning for Smart and Real-Time Irrigation Management: A Review. Sensors. 2024; 24(23):7480. https://doi.org/10.3390/s24237480

Chicago/Turabian Style

Nsoh, Bryan, Abia Katimbo, Hongzhi Guo, Derek M. Heeren, Hope Njuki Nakabuye, Xin Qiao, Yufeng Ge, Daran R. Rudnick, Joshua Wanyama, Erion Bwambale, and et al. 2024. "Internet of Things-Based Automated Solutions Utilizing Machine Learning for Smart and Real-Time Irrigation Management: A Review" Sensors 24, no. 23: 7480. https://doi.org/10.3390/s24237480

APA Style

Nsoh, B., Katimbo, A., Guo, H., Heeren, D. M., Nakabuye, H. N., Qiao, X., Ge, Y., Rudnick, D. R., Wanyama, J., Bwambale, E., & Kiraga, S. (2024). Internet of Things-Based Automated Solutions Utilizing Machine Learning for Smart and Real-Time Irrigation Management: A Review. Sensors, 24(23), 7480. https://doi.org/10.3390/s24237480

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