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Review
. 2024 Nov 23;24(23):7480.
doi: 10.3390/s24237480.

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 , Shafik Kiraga  8
Affiliations
Review

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

Bryan Nsoh et al. Sensors (Basel). .

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: artificial intelligence; crop productivity; edge computing; interoperability; precision agriculture; precision irrigation; remote monitoring; sensor networks; smart farming; water use efficiency.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Flowchart depicting the literature search and selection process.
Figure 2
Figure 2
Automated irrigation system architecture depicting the flow from data collection through IoT devices, transmission via various networks, cloud processing, decision-making, to precision application.
Figure 3
Figure 3
Common data types, sources, and collection methods in automated irrigation systems.
Figure 4
Figure 4
Containerized irrigation management system. Just as shipping containers standardize cargo transport, software containers can package irrigation system’s software components. Each container (color-coded) encapsulates a specific function, allowing for efficient deployment and scalability across the system, orchestrated by systems like Kubernetes.
Figure 5
Figure 5
Automated data processing pipeline in the cloud for irrigation management.
Figure 6
Figure 6
Comparison of irrigation technologies across four key metrics. Longer bars indicate better performance (higher potential/efficiency, higher cost-effectiveness, lower maintenance). Data synthesized from Musick et al. [118], C’orcoles et al. [119], and Evans [120]. Specific values may vary based on implementation and local conditions.
Figure 7
Figure 7
Fault tolerance techniques for improving reliability in precision irrigation systems.
See this image and copyright information in PMC

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