Internet of Things-Based Automated Solutions Utilizing Machine Learning for Smart and Real-Time Irrigation Management: A Review
Bryan Nsoh,
Abia Katimbo,
Hongzhi Guo,
Derek M. Heeren,
Hope Njuki Nakabuye,
Xin Qiao,
Yufeng Ge,
Daran R. Rudnick,
Joshua Wanyama,
Erion Bwambale,
Shafik Kiraga
Affiliations
Bryan Nsoh
Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
Abia Katimbo
Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
Hongzhi Guo
School of Computing, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
Derek M. Heeren
Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
Hope Njuki Nakabuye
Texas A&M AgriLife, 1102 East Drew Street, Lubbock, TX 79403, USA
Xin Qiao
Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
Yufeng Ge
Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
Daran R. Rudnick
Carl and Melinda Helwig Department of Biological and Agricultural Engineering, Kansas State University, Manhattan, KS 66506, USA
Joshua Wanyama
Department of Agricultural and Biosystems Engineering, Makerere University, Kampala P.O. Box 7062, Uganda
Erion Bwambale
Department of Agricultural and Biosystems Engineering, Makerere University, Kampala P.O. Box 7062, Uganda
Shafik Kiraga
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
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.
