The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
VGG-ICNN: A Lightweight CNN model for crop disease identification
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Citations
Cites
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
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
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
Ma, N., Zhang, X., Zheng, H.-T., & Sun, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01264-9_8 · Cited by 7,356 · cited 3×
“MobileNet v2 [43], ShuffleNet v2 [35], EfficientNet B0 [48], GhostNet [17], and Ramamurthy et al.”
BackgroundMethodGhostNet: More Features From Cheap Operations
Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., & Xu, C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00165 · Cited by 5,222
Using Deep Learning for Image-Based Plant Disease Detection
Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016) · Frontiers in Plant Science · doi:10.3389/fpls.2016.01419 · Cited by 5,026
The global burden of pathogens and pests on major food crops
Savary, S., Willocquet, L., Pethybridge, S. J., Esker, P., McRoberts, N., & Nelson, A. (2019) · Nature Ecology & Evolution · doi:10.1038/s41559-018-0793-y · Cited by 4,309
Deep learning models for plant disease detection and diagnosis
Ferentinos, K. P. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.01.009 · Cited by 3,390
A comparative study of fine-tuning deep learning models for plant disease identification
Too, E. C., Yujian, L., Njuki, S., & Yingchun, L. (2019) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.03.032 · Cited by 1,487
Identification of rice diseases using deep convolutional neural networks
Lu, Y., Yi, S., Zeng, N., Liu, Y., & Zhang, Y. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.06.023 · Cited by 1,059
Metadata
- Authors
- Thakur, P. S., Sheorey, T., & Ojha, A.
- Venue
- Multimedia Tools and Applications
- Published
- 2022-06-07
- Volume
- 82
- Issue
- 1
- Pages
- 497-520
- ISSN
- 1380-7501 (ISSN-L)
- 1573-7721
- OpenAlex
- W4281846758
- Cited by
- 259
- References
- 41
- FWCI
- 25.58
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
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- CoChat. (2026). Citation check: VGG-ICNN: A Lightweight CNN model for crop disease identification. https://app.cochat.ai/papers/10.1007/s11042-022-13144-z
- The paper
- Thakur, P. S., Sheorey, T., & Ojha, A. (2023). VGG-ICNN: A Lightweight CNN model for crop disease identification. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-022-13144-z
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.
