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

Aug 6, 2026

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This systematic review critically evaluates the current state and future potential of real-time, end-to-end smart and automated irrigation management systems. Focus centers on integrating the Internet of Things and machine learning to improve agricultural water use efficiency and crop productivity. The review examines how automation is implemented throughout the irrigation pipeline from data collection to application, assessing effectiveness, efficiency, and precision-agriculture integration. It also addresses interoperability, standardization, and cybersecurity, identifies research gaps, and proposes scalable solutions for seamless multi-sensor integration.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages38

Reading Time1 h 36 min

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Department of Biological Systems Engineering: Papers and Publications Biological Systems Engineering, Department of 11-8-2024 Internet of Things-Based Automated Solutions Utilizing Machine Learning for Smart and Real-Time Irrigation Management: AReview Bryan Nsoh Abia Katimbo Hongzhi Guo Derek M. Heeren Hope Njuki Nakabuye See next page for additional authors Follow this and additional works at: [https://digitalcommons.unl.edu/biosysengfacpub](https://digitalcommons.unl.edu/biosysengfacpub) Part of the Bioresource and Agricultural Engineering Commons, Environmental Engineering Commons, and the Other Civil and Environmental Engineering Commons This Article is brought to you for free and open access by the Biological Systems Engineering, Department of at DigitalCommons@University of Nebraska-Lincoln. It has been accepted for inclusion in Department of Biological Systems Engineering: Papers and Publications by an authorized administrator of DigitalCommons@University of Nebraska-Lincoln. Authors Bryan Nsoh, Abia Katimbo, Hongzhi Guo, Derek M. Heeren, Hope Njuki Nakabuye, Xin qiao, Yufeng Ge, Daran R. Rudnick, Joshua Wanyama, Erion Bwambale, and Shafik Kiraga Review Internet of Things-Based Automated Solutions Utilizing Machine Learning for Smart and Real-Time Irrigation Management: A Review 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 Citation: 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/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)s24237480 Academic Editors: Gang Wang and Jiangtao Qi Received: 26 August 2024 Revised: 4 November 2024 Accepted: 8 November 2024 Published: 23 November 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) . 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; [hopenjuki.nakabuye@ag.tamu.edu](hopenjuki.nakabuye@ag.tamu.edu) [5](5 Panhandle Research)[ Panhandle Research](5 Panhandle Research), [Extension](Extension), [and Education Center](and Education Center), [University of Nebraska-Lincoln](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; [wanyama2002@gmail.com](wanyama2002@gmail.com) (J.W.) 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 * Correspondence: [abia.katimbo@unl.edu](abia.katimbo@unl.edu) 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

FAQs

What is the main focus of this review on irrigation management?

It evaluates real-time, end-to-end smart and automated irrigation management systems, emphasizing the integration of IoT and machine learning.

How does the review analyze automation in irrigation systems?

It examines automation across the irrigation pipeline from data collection to application, evaluating effectiveness, efficiency, and integration with precision agriculture technologies.

Which key enabling issues does the review address for IoT-based irrigation solutions?

It investigates interoperability, standardization, and cybersecurity, and proposes approaches for seamless integration across multiple sensor suites.

Aug 6, 2026

Upload by Eliana