IoT-Enabled Smart Agriculture: Architecture, Applications, and Challenges
Open accessAbstract
The growth of the global population coupled with a decline in natural resources, farmland, and the increase in unpredictable environmental conditions leads to food security is becoming a major concern for all nations worldwide. These problems are motivators that are driving the agricultural industry to transition to smart agriculture with the application of the Internet of Things (IoT) and big data solutions to improve operational efficiency and productivity. The IoT integrates a series of existing state-of-the-art solutions and technologies, such as wireless sensor networks, cognitive radio ad hoc networks, cloud computing, big data, and end-user applications. This study presents a survey of IoT solutions and demonstrates how IoT can be integrated into the smart agriculture sector. To achieve this objective, we discuss the vision of IoT-enabled smart agriculture ecosystems by evaluating their architecture (IoT devices, communication technologies, big data storage, and processing), their applications, and research timeline. In addition, we discuss trends and opportunities of IoT applications for smart agriculture and also indicate the open issues and challenges of IoT application in smart agriculture. We hope that the findings of this study will constitute important guidelines in research and promotion of IoT solutions aiming to improve the productivity and quality of the agriculture sector as well as facilitating the transition towards a future sustainable environment with an agroecological approach.
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Many SF applications use three-layer architecture [114].
“Many SF applications use three-layer architecture [114](see Fig. 10).”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [114]
The passage accompanying Figure 2 (indexed under Conclusions; embedded pp. 3–4) presents a smart-agriculture IoT ecosystem with three components, including communication technologies and data processing and storage. It does not establish that many smart-farming applications use a three-layer architecture; components of a depicted ecosystem are not necessarily layers used across applications. (LLM, conf=0.78)
Experimental — accuracy not yet measuredPoor internet connection causes issues such as huge latency, data loss and reliability issues [114].
“Poor internet connection causes some issues such as huge latency, data loss and the reliability issues [114].”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [114]
Section 4.2 (Technical Problems; page unavailable) says network interference can degrade IoT system performance and reliability, supporting the reliability concern. It does not say that poor internet connection causes huge latency or data loss; searches for those terms found no supporting passage. (LLM, conf=0.82)
Experimental — accuracy not yet measuredTinyML offers several advantages over cloud, fog, and edge computing in terms of privacy, security, latency, and energy consumption [114].
“TinyML offers several advantages over cloud, fog, and edge computing in terms of privacy, security, latency, and energy consumption [114].”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [114]
Section 4.2 discusses farm-data privacy and security, and Section 2.2 discusses communication energy efficiency, but neither addresses TinyML. Searches for “TinyML” and on-device machine learning found no passage comparing it with cloud, fog, or edge computing on the claimed advantages. (LLM, conf=0.76)
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Variability of Terahertz Transmission Measured in Live Plant Leaves
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Application and Research Progress of Geographic Information System (GIS) in Agriculture
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A High-End IoT Devices Framework to Foster Beyond-Connectivity Capabilities in 5G/B5G Architecture
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Statistical Qos Guarantees for Licensed-Unlicensed Spectrum Interoperable D2D Communication
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NFMI: Connectivity for Short-Range IoT Applications
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A Comprehensive Survey on TinyML
Abadade, Y., Temouden, A., Bamoumen, H., Benamar, N., Chtouki, Y., & Hafid, A. S. (2023) · IEEE Access · doi:10.1109/access.2023.3294111 · Cited by 366 · see citing works
“cyber-attack threats and decrease data protection [114].”
The impact of 6G-IoT technologies on the development of agriculture 5.0: a review
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- Authors
- Quy, V. K., Hau, N. V., Anh, D. V., Quy, N. M., Ban, N. T., Lanza, S., Randazzo, G., & Muzirafuti, A.
- Affiliations
- Faculty of Information Technology, Hung Yen University of Technology and Education, Hung Yen 160000, Vietnam
- Hung Yen University of Technology and Education (VN)
- Posts and Telecommunications Institute of Technology (VN)
- University of Messina (IT)
- Corresponding author
- Muzirafuti, Anselme
- ORCID
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2022-03-27
- Volume
- 12
- Issue
- 7
- Pages
- 3396
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W4220938570
- Cited by
- 458
- References
- 124
- Citations by year
- 2026: 60 · 2025: 133 · 2024: 129 · 2023: 102 · 2022: 33 · 2019: 1
- FWCI
- 54.65
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Applied Sciences (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- Topics
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- smart agriculture, Internet of Things, IoT architecture, wireless sensor networks, cognitive radio ad hoc networks, cloud computing, big data, food security, agricultural productivity, sustainable agriculture, agroecology
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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