The DOI matches its Crossref record; 68 of the 68 references it cites were found in a registry or an index.
An overview of smart irrigation systems using IoT
Open accessAbstract
Countries are working into making agriculture more sustainable by integrating different technologies to enhance its operation. Implementing improvements in irrigation systems is crucial for the water-use efficiency and works as a contributor to Sustainable Development Goals (SDGs) under the United Nations specifically Goal 6 and Target 6.4. This paper aims to highlight the contribution of SMART irrigation using Internet of Things (IoT) and sensory systems in relation to the SDGs. The study is based on a qualitative design along with focusing on secondary data collection method. Automated irrigation systems are essential for conservation of water, this improvement could have a vital role in minimizing water usage. Agriculture and farming techniques is also linked with IoT and automation, to make the whole processes much more effective and efficient. Sensory systems helped farmers better understand their crops and reduced the environmental impacts and conserve resources. Through these advanced systems effective soil and weather monitoring takes place along with efficient water management. Irrigation systems have been determined as positive contributor toward optimized irrigation systems that could enhance the use of continuous research and development which focus on enhancing the sustainable operations and cost reduction. Lastly, the challenges and benefits for the implementation of sensory based irrigation systems are discussed. This review will assist researchers and farmers to better understand irrigation techniques and provide an adequate approach would be sufficient to carry out irrigation related activities.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Machine Learning in Agriculture: A Review
Liakos, K., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018) · Sensors · doi:10.3390/s18082674 · Cited by 3,441
Chlingaryan, A., Sukkarieh, S., & Whelan, B. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.05.012 · Cited by 1,577
An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges
Elijah, O., Rahman, T. A., Orikumhi, I., Leow, C. Y., & Hindia, M. H. D. N. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2844296 · Cited by 1,467
Internet-of-Things (IoT)-Based Smart Agriculture: Toward Making the Fields Talk
Ayaz, M., Ammad-Uddin, M., Sharif, Z., Mansour, A., & Aggoune, E.-H. M. (2019) · IEEE Access · doi:10.1109/access.2019.2932609 · Cited by 1,306
A comprehensive review on automation in agriculture using artificial intelligence
Jha, K., Doshi, A., Patel, P., & Shah, M. (2019) · Artificial Intelligence in Agriculture · doi:10.1016/j.aiia.2019.05.004 · Cited by 1,005
Wireless sensor networks for agriculture: The state-of-the-art in practice and future challenges
Ojha, T., Misra, S., & Raghuwanshi, N. S. (2015) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2015.08.011 · Cited by 894
Microplastics as pollutants in agricultural soils
Kumar, M., Xiong, X., He, M., Tsang, D. C. W., Gupta, J., Khan, E., Harrad, S., Hou, D., Ok, Y. S., & Bolan, N. S. (2020) · Environmental Pollution · doi:10.1016/j.envpol.2020.114980 · Cited by 802
Ayoub Shaikh, T., Rasool, T., & Rasheed Lone, F. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.107119 · Cited by 768
García, L., Parra, L., Jimenez, J. M., Lloret, J., & Lorenz, P. (2020) · Sensors · doi:10.3390/s20041042 · Cited by 758
An IoT based smart irrigation management system using Machine learning and open source technologies
Goap, A., Sharma, D., Shukla, A. K., & Rama Krishna, C. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.09.040 · Cited by 676
Metadata
- Authors
- Obaideen, K., Yousef, B. A. A., AlMallahi, M. N., Tan, Y. C., Mahmoud, M., Jaber, H., & Ramadan, M.
- Venue
- Energy Nexus
- Publisher
- Elsevier BV
- Published
- 2022-09
- Volume
- 7
- Pages
- 100124
- ISSN
- 2772-4271 (ISSN-L)
- OpenAlex
- W4287843580
- Cited by
- 448
- References
- 68
- FWCI
- 43.00
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: An overview of smart irrigation systems using IoT. https://app.cochat.ai/papers/10.1016/j.nexus.2022.100124
- The paper
- Obaideen, K., Yousef, B. A. A., AlMallahi, M. N., Tan, Y. C., Mahmoud, M., Jaber, H., & Ramadan, M. (2022). An overview of smart irrigation systems using IoT. Energy Nexus. https://doi.org/10.1016/j.nexus.2022.100124
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.