The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
A Novel Deep Learning Model for Detection of Severity Level of the Disease in Citrus Fruits
Open accessAbstract
Citrus fruit diseases have an egregious impact on both the quality and quantity of the citrus fruit production and market. Automatic detection of severity is essential for the high-quality production of fruit. In the current work, a citrus fruit dataset is preprocessed by rescaling and establishing bounding boxes with labeled image software. Then, a selective search, which combines the capabilities of both an extensive search and graph-based segmentation, is applied. The proposed deep neural network (DNN) model is trained to detect targeted areas of the disease with its severity level using citrus fruits that have been labeled with the help of a domain expert with four severity levels (high, medium, low and healthy) as ground truth. Transfer learning using VGGNet is applied to implement a multi-classification framework for each class of severity. The model predicts the low severity level with 99% accuracy, and the high severity level with 98% accuracy. The model demonstrates 96% accuracy in detecting healthy conditions and 97% accuracy in detecting medium severity levels. The result of the work shows that the proposed approach is valid, and it is efficient for detecting citrus fruit disease at four levels of severity.
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
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017623 · Cited by 15,398
Selective Search for Object Recognition
Uijlings, J. R. R., van de Sande, K. E. A., Gevers, T., & Smeulders, A. W. M. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0620-5 · Cited by 5,980
Acquisition of Localization Confidence for Accurate Object Detection
Jiang, B., Luo, R., Mao, J., Xiao, T., & Jiang, Y. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01264-9_48 · Cited by 1,059
Using deep transfer learning for image-based plant disease identification
Chen, J., Chen, J., Zhang, D., Sun, Y., & Nanehkaran, Y. A. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105393 · Cited by 1,052
A Proof of Local Convergence for the Adam Optimizer
Bock, S., & Weis, M. (2019) · 2019 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8852239 · Cited by 332
Ozguven, M. M., & Adem, K. (2019) · Physica A: Statistical Mechanics and its Applications · doi:10.1016/j.physa.2019.122537 · Cited by 299
Rauf, H. T., Saleem, B. A., Lali, M. I. U., Khan, M. A., Sharif, M., & Bukhari, S. A. C. (2019) · Data in Brief · doi:10.1016/j.dib.2019.104340 · Cited by 274
Exploit Bounding Box Annotations for Multi-Label Object Recognition
Yang, H., Zhou, J. T., Zhang, Y., Gao, B.-B., Wu, J., & Cai, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.37 · Cited by 180
Wang, Q., Qi, F., Sun, M., Qu, J., & Xue, J. (2019) · Computational Intelligence and Neuroscience · doi:10.1155/2019/9142753 · Cited by 180
Jahanbakhshi, A., Momeny, M., Mahmoudi, M., & Zhang, Y.-D. (2020) · Scientia Horticulturae · doi:10.1016/j.scienta.2019.109133 · Cited by 163
Comparison of machine learning methods for citrus greening detection on UAV multispectral images
Lan, Y., Huang, Z., Deng, X., Zhu, Z., Huang, H., Zheng, Z., Lian, B., Zeng, G., & Tong, Z. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105234 · Cited by 153
Real time implementation of RGB to HSV/HSI/HSL and its reverse color space models
Saravanan, G., Yamuna, G., & Nandhini, S. (2016) · doi:10.1109/iccsp.2016.7754179 · Cited by 148
A Smartphone-Based Application for Scale Pest Detection Using Multiple-Object Detection Methods
Chen, J.-W., Lin, W.-J., Cheng, H.-J., Hung, C., Lin, C., & Chen, S.-P. (2021) · Electronics · doi:10.3390/electronics10040372 · Cited by 141
Cubero, S., Lee, W. S., Aleixos, N., Albert, F. J., & Blasco, J. (2016) · Food and Bioprocess Technology · doi:10.1007/s11947-016-1767-1 · Cited by 139
Lorente, D., Aleixos, N., Gómez‐Sanchís, J., Cubero, S., & Blasco, J. (2011) · Food and Bioprocess Technology · doi:10.1007/s11947-011-0737-x · Cited by 134
Lorente, D., Escandell-Montero, P., Cubero, S., Gómez‐Sanchís, J., & Blasco, J. (2015) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2015.04.010 · Cited by 98
Automatic recognition of quarantine citrus diseases
Stegmayer, G. S., Milone, D. H., Garrán, S., & Burdyn, L. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.12.059 · Cited by 73
Han, L., Haleem, M. S., & Taylor, M. C. (2015) · Science and Information Conference · doi:10.1109/sai.2015.7237209 · Cited by 61
CNN transfer learning for the automated diagnosis of celiac disease
Wimmer, G., Vécsei, A., & Uhl, A. (2016) · International Conference on Image Processing Theory, Tools and Applications (IPTA) · doi:10.1109/ipta.2016.7821020 · Cited by 50
Metadata
- Authors
- Dhiman, P., Kukreja, V., Manoharan, P., Kaur, A., Kamruzzaman, M. M., Dhaou, I. B., & Iwendi, C.
- Venue
- Electronics
- Publisher
- MDPI AG
- Published
- 2022-02-08
- Volume
- 11
- Issue
- 3
- Pages
- 495
- ISSN
- 2079-9292 (ISSN-L)
- OpenAlex
- W4211112186
- Cited by
- 308
- References
- 20
- FWCI
- 34.72
- Type
- journal article
- Language
- en
- Open access
- gold
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
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.
