Internet of Things-Based Automated Solutions Utilizing Machine Learning for Smart and Real-Time Irrigation Management: A Review Article Swipe
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
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/s24237480
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/s24237480
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.
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Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.3390/s24237480
- OA Status
- gold
- Cited By
- 28
- References
- 169
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4404692634Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/s24237480Digital Object Identifier
- Title
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Internet of Things-Based Automated Solutions Utilizing Machine Learning for Smart and Real-Time Irrigation Management: A ReviewWork title
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-11-23Full publication date if available
- Authors
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Bryan Nsoh, Abia Katimbo, Hongzhi Guo, Derek M. Heeren, Hope Njuki Nakabuye, Xin Qiao, Yufeng Ge, Daran R. Rudnick, Joshua Wanyama, Erion Bwambale, Shafik KiragaList of authors in order
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.3390/s24237480Direct OA link when available
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Interoperability, Standardization, Automation, Computer science, Precision agriculture, Scalability, Systems engineering, Engineering, Agriculture, Database, World Wide Web, Operating system, Biology, Mechanical engineering, EcologyTop concepts (fields/topics) attached by OpenAlex
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28Total citation count in OpenAlex
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2025: 27, 2024: 1Per-year citation counts (last 5 years)
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169Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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