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LiteCShuffle: a lightweight and efficient deep learning approach for plant disease classification
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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 · cited 8×
“Conventional architectures, such as VGG16 and Densenet121, are known to be computationally intensive, making them less suitable for real-world agricultural environments where processing resources are typically constrained.”
InfluentialImageNet 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 · cited 1×
“Group convolution was initially suggested in alexnet architecture (Krizhevsky et al., 2017).”
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
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 · cited 1×
“For improved multi-disease classification, study (Chai et al., 2025) (2016). in table 1, a brief overview of approaches presented in recent studies is discussed along with their performance metrics on the varied datasets used and the research gaps present.”
Feature Pyramid Networks for Object Detection
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939 · cited 2×
“…in order to resolve this problem, researchers are proposing hybrid models that combine various methodologies such as Feature pyramid networks (Fpn) (lin et al., 2017), attention mechanisms, Support Vector Machines (SVM) (hearst et al., 1998), and few-shot learning, which have proven to enhance…”
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 6×
“Some lightweight models like MobilenetV2 (Sandler et al., 2018), ShufflenetV2 (Ma et al., 2018), efficientnet B0 (tan & le, 2020), and Ghostnet (han et al., 2020) shows good performance. nevertheless, these standard models contain millions of parameters, which means the entire model size is…”
Influential
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Metadata
- Authors
- Duhan, S., Gulia, P., Gill, N. S., Aziz, A. L., Aljuaid, M., Banshal, S. K., & Bansal, R.
- Venue
- Cogent Food & Agriculture
- Publisher
- Informa UK Limited
- Published
- 2025-10-10
- Volume
- 11
- Issue
- 1
- ISSN
- 2331-1932
- Cited by
- 4
- References
- 7
- Language
- en
- Funders
- Universitas Brawijaya
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- Duhan, S., Gulia, P., Gill, N. S., Aziz, A. L., Aljuaid, M., Banshal, S. K., & Bansal, R. (2025). LiteCShuffle: a lightweight and efficient deep learning approach for plant disease classification. Cogent Food & Agriculture. https://doi.org/10.1080/23311932.2025.2568197
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