The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
A deep learning based approach for automated plant disease classification using vision transformer
Open accessAbstract
Plant disease can diminish a considerable portion of the agricultural products on each farm. The main goal of this work is to provide visual information for the farmers to enable them to take the necessary preventive measures. A lightweight deep learning approach is proposed based on the Vision Transformer (ViT) for real-time automated plant disease classification. In addition to the ViT, the classical convolutional neural network (CNN) methods and the combination of CNN and ViT have been implemented for the plant disease classification. The models have been trained and evaluated on multiple datasets. Based on the comparison between the obtained results, it is concluded that although attention blocks increase the accuracy, they decelerate the prediction. Combining attention blocks with CNN blocks can compensate for the speed.
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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: A large-scale hierarchical image database
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Rethinking the Inception Architecture for Computer Vision
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Backpropagation Applied to Handwritten Zip Code Recognition
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Self-Training With Noisy Student Improves ImageNet Classification
Xie, Q., Luong, M.-T., Hovy, E., & Le, Q. V. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01070 · Cited by 2,393
Metadata
- Authors
- Borhani, Y., Khoramdel, J., & Najafi, E.
- Venue
- Scientific Reports
- Published
- 2022-07-07
- Volume
- 12
- Issue
- 1
- Pages
- 11554-11554
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W4284670866
- Cited by
- 306
- References
- 6
- FWCI
- 27.90
- Type
- journal article
- Language
- en
- Open access
- gold
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