The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
A deep learning approach for RGB image-based powdery mildew disease detection on strawberry leaves
Abstract
Abstract In this study, Deep Learning (DL) was used to detect powdery mildew (PM), persistent fungal disease in strawberries to reduce the amount of unnecessary fungicide use, and the need for field scouts. This study optimised and evaluated several well-established learners, including AlexNet, SqueezeNet, GoogLeNet, ResNet-50, SqueezeNet-MOD1, and SqueezeNet-MOD2. Data augmentation was carried out from among 1450 healthy and infected leaf images to prevent overfitting and to consider the various shapes and direction of the leaves in the field. A total of eight clockwise rotations (0°; the original data, 45°, 90°, 135°, 180°, 225°, 270°, and 315°) was performed to generate 11,600 data points. Overall, the six DL algorithms that were used in this study showed on average of >92% in classification accuracy (CA). ResNet-50 gave the highest CA of 98.11% in classifying the healthy and infected leaves; however, considering the computation time, AlexNet had the fastest processing time, at 40.73 s, to process 2320 images with a CA of 95.59%.When considering the memory requirements for hardware deployment, SqueezeNet-MOD2 would be recommended for PM detection on strawberry leaves with a CA of 92.61%.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
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
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.369 · Cited by 4,616
Deep learning-based classification of hyperspectral data
Chen, Y., Lin, Z., Zhao, X., Wang, G., & Gu, Y. (2014) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2014.2329330 · Cited by 2,679
Benchmark analysis of representative deep neural network architectures
Bianco, S., Cadene, R., Celona, L., & Napoletano, P. (2018) · IEEE Access · doi:10.1109/access.2018.2877890 · Cited by 838
Improving Deep Learning with Generic Data Augmentation
Taylor, L., & Nitschke, G. (2018) · 2018 IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2018.8628742 · Cited by 796
Data Augmentation for Low-Resource Neural Machine Translation
Fadaee, M., Bisazza, A., & Monz, C. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/p17-2090 · Cited by 266
Cited by
Recognition of rice leaf diseases and wheat leaf diseases based on multi-task deep transfer learning
Jiang, Z., Dong, Z., Jiang, W., & Yang, Y. (2021) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2021.106184 · Cited by 242
Metadata
- Authors
- Shin, J., Chang, Y. K., Heung, B., Nguyen-Quang, T., Price, G. W., & Al-Mallahi, A.
- Venue
- Computers and Electronics in Agriculture
- Publisher
- Elsevier BV
- Published
- 2021-04
- Volume
- 183
- Pages
- 106042
- ISSN
- 0168-1699 (ISSN-L)
- OpenAlex
- W3134717070
- Cited by
- 191
- References
- 8
- FWCI
- 17.16
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A deep learning approach for RGB image-based powdery mildew disease detection on strawberry leaves. https://app.cochat.ai/papers/10.1016/j.compag.2021.106042
- The paper
- Shin, J., Chang, Y. K., Heung, B., Nguyen-Quang, T., Price, G. W., & Al-Mallahi, A. (2021). A deep learning approach for RGB image-based powdery mildew disease detection on strawberry leaves. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2021.106042
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.
