The DOI matches its Crossref record; 158 of the 158 references it cites were found in a registry or an index.
A survey on the role of Internet of Things for adopting and promoting Agriculture 4.0
Abstract
Abstract There is a rapid increase in the adoption of emerging technologies like the Internet of Things (IoT), Unmanned Aerial Vehicles (UAV), Internet of Underground Things (IoUT), Data analytics in the agriculture domain to meet the increased food demand to cater to the increasing population. Agriculture 4.0 is set to revolutionize agriculture productivity by using Precision Agriculture (PA), IoT, UAVs, IoUT, and other technologies to increase agriculture produce for growing demographics while addressing various farm-related issues. This survey provides a comprehensive overview of how multiple technologies such as IoT, UAVs, IoUT, Big Data Analytics, Deep Learning Techniques, and Machine Learning methods can be used to manage various farm-related operations. For each of these technologies, a detailed review is done on how the technology is being used in Agriculture 4.0. These discussions include an overview of relevant technologies, their use cases, existing case studies, and research works that demonstrate the use of these technologies in Agriculture 4.0. This paper also highlights the various future research gaps in the adoption of these technologies in Agriculture 4.0.
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
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 22,066
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1993) · Proceedings of IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.1993.341010 · Cited by 14,265
Overview of the radiometric and biophysical performance of the MODIS vegetation indices
Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002) · Remote Sensing of Environment · doi:10.1016/s0034-4257(02)00096-2 · Cited by 9,869
Cited by
Enhancing smart farming through the applications of Agriculture 4.0 technologies
Javaid, M., Haleem, A., Singh, R. P., & Suman, R. (2022) · International Journal of Intelligent Networks · doi:10.1016/j.ijin.2022.09.004 · Cited by 573
A comparative study of deep learning and internet of things for precision agriculture
Saranya, T., Deisy, C., Sridevi, S., & Anbananthen, K. S. M. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106034 · Cited by 232
Application of Internet of Things (IoT) for Optimized Greenhouse Environments
Maraveas, C., & Bartzanas, T. (2021) · AgriEngineering · doi:10.3390/agriengineering3040060 · Cited by 106
Intelligent and Efficient IoT Through the Cooperation of TinyML and Edge Computing
Sanchez-Iborra, R., Zoubir, A., Hamdouchi, A., Idri, A., & Skarmeta, A. (2023) · Informatica · doi:10.15388/22-infor505 · Cited by 28
Metadata
- Authors
- Raj, M., Gupta, S., Chamola, V., Elhence, A., Garg, T., Atiquzzaman, M., & Niyato, D.
- Venue
- Journal of Network and Computer Applications
- Publisher
- Elsevier BV
- Published
- 2021-08
- Volume
- 187
- Pages
- 103107
- ISSN
- 1084-8045 (ISSN-L)
- OpenAlex
- W3161987923
- Cited by
- 283
- References
- 158
- FWCI
- 23.67
- Type
- journal article
- Language
- en
- Open access
- closed
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: A survey on the role of Internet of Things for adopting and promoting Agriculture 4.0. https://app.cochat.ai/papers/10.1016/j.jnca.2021.103107
- The paper
- Raj, M., Gupta, S., Chamola, V., Elhence, A., Garg, T., Atiquzzaman, M., & Niyato, D. (2021). A survey on the role of Internet of Things for adopting and promoting Agriculture 4.0. Journal of Network and Computer Applications. https://doi.org/10.1016/j.jnca.2021.103107
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.
