The DOI matches its Crossref record; 151 of the 151 references it cites were found in a registry or an index.
Automation in Agriculture by Machine and Deep Learning Techniques: A Review of Recent Developments
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
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
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
(2016) · Cited by 31,590
MobileNetV2: Inverted Residuals and Linear Bottlenecks
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00474 · Cited by 28,755
Cited by
A review of deep learning techniques used in agriculture
Attri, I., Awasthi, L. K., Sharma, T. P., & Rathee, P. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102217 · Cited by 336
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
Metadata
- Authors
- Saleem, M. H., Potgieter, J., & Arif, K. M.
- Venue
- Precision Agriculture
- Published
- 2021-04-21
- Volume
- 22
- Issue
- 6
- Pages
- 2053-2091
- ISSN
- 1385-2256 (ISSN-L)
- 1573-1618
- OpenAlex
- W3156943756
- Cited by
- 434
- References
- 151
- FWCI
- 31.92
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Automation in Agriculture by Machine and Deep Learning Techniques: A Review of Recent Developments. https://app.cochat.ai/papers/10.1007/s11119-021-09806-x
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- Saleem, M. H., Potgieter, J., & Arif, K. M. (2021). Automation in Agriculture by Machine and Deep Learning Techniques: A Review of Recent Developments. Precision Agriculture. https://doi.org/10.1007/s11119-021-09806-x
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.
