The DOI matches its Crossref record; 121 of the 121 references it cites were found in a registry or an index.
A survey of deep learning techniques for weed detection from images
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Cites
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
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
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
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
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
(2016) · Cited by 31,590
Cited by
Dang, F., Chen, D., Lu, Y., & Li, Z. (2023) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2023.107655 · Cited by 313
Review of Weed Detection Methods Based on Computer Vision
Wu, Z., Chen, Y., Zhao, B., Kang, X., & Ding, Y. (2021) · Sensors · doi:10.3390/s21113647 · Cited by 303
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
- Hasan, A. S. M. M., Sohel, F., Diepeveen, D., Laga, H., & Jones, M. G. K.
- Venue
- Computers and Electronics in Agriculture
- Publisher
- Elsevier BV
- Published
- 2021-05
- Volume
- 184
- Pages
- 106067
- ISSN
- 0168-1699 (ISSN-L)
- OpenAlex
- W3134686538
- Cited by
- 478
- References
- 121
- FWCI
- 42.83
- Type
- journal article
- Language
- en
- Open access
- green
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- The paper
- Hasan, A. S. M. M., Sohel, F., Diepeveen, D., Laga, H., & Jones, M. G. K. (2021). A survey of deep learning techniques for weed detection from images. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2021.106067
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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.
