The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Tomato plant disease detection using transfer learning with C-GAN synthetic 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
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
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
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138
Xception: Deep Learning with Depthwise Separable Convolutions
Chollet, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.195 · Cited by 20,380
Conditional Generative Adversarial Nets
Mirza, M., & Osindero, S. (2014) · arXiv · doi:10.48550/arxiv.1411.1784 · Cited by 8,863
Cited by
Transfer learning in environmental remote sensing
Ma, Y., Chen, S., Ermon, S., & Lobell, D. B. (2024) · Remote Sensing of Environment · doi:10.1016/j.rse.2023.113924 · Cited by 425
Deep Learning-Based Leaf Disease Detection in Crops Using Images for Agricultural Applications
J., A., Eunice, J., Popescu, D. E., Chowdary, M. K., & Hemanth, J. (2022) · Agronomy · doi:10.3390/agronomy12102395 · Cited by 409
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
Deep diagnosis: A real-time apple leaf disease detection system based on deep learning
Khan, A. I., Quadri, S. M. K., Banday, S., & Latief Shah, J. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.107093 · Cited by 264
Metadata
- Authors
- Abbas, A., Jain, S., Gour, M., & Vankudothu, S.
- Venue
- Computers and Electronics in Agriculture
- Publisher
- Elsevier BV
- Published
- 2021-08
- Volume
- 187
- Pages
- 106279
- ISSN
- 0168-1699 (ISSN-L)
- OpenAlex
- W3173170759
- Cited by
- 651
- References
- 8
- FWCI
- 56.79
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Abbas, A., Jain, S., Gour, M., & Vankudothu, S. (2021). Tomato plant disease detection using transfer learning with C-GAN synthetic images. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2021.106279
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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.
