The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms
Abstract
Abstract Depending on the severity of the leaf spot disease in the field, it can cause a loss in sugar yield by 10% to 50%. Therefore, disease symptoms should be detected on-time and relevant measures should be taken instantly to prevent further spread or progress of the disease. In this study, an Updated Faster R-CNN architecture developed by changing the parameters of a CNN model and a Faster R-CNN architecture for automatic detection of leaf spot disease (Cercospora beticola Sacc.) in sugar beet were proposed. The method, proposed for the detection of disease severity by imaging-based expert systems, was trained and tested with 155 images and according to the test results, the overall correct classification rate was found to be 95.48%. In addition, the proposed approach showed that changes in CNN parameters according to the image and regions to be detected could increase the success of Faster R-CNN architecture. The proposed approach yielded better outcomes for relevant parameters than the modern methods specified in previous literature. Therefore, it is believed that the method will reduce the time spent in diagnosis of sugar beet leaf spot disease in the large production areas as well as reducing the human error and time to identify the severity and course of the disease.
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
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105
Convolutional Neural Networks for Sentence Classification
Kim, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1181 · Cited by 14,831
Deep learning in agriculture: A survey
Kamilaris, A., & Prenafeta-Boldú, F. X. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.02.016 · Cited by 5,133
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
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
Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification
Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., & Stefanovic, D. (2016) · Computational Intelligence and Neuroscience · doi:10.1155/2016/3289801 · Cited by 2,029
A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition
Fuentes, A., Yoon, S., Kim, S., & Park, D. (2017) · Sensors · doi:10.3390/s17092022 · Cited by 1,572
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
Cited by
Plant diseases and pests detection based on deep learning: a review
Liu, J., & Wang, X. (2021) · Plant Methods · doi:10.1186/s13007-021-00722-9 · Cited by 1,011
A Novel Deep Learning Model for Detection of Severity Level of the Disease in Citrus Fruits
Dhiman, P., Kukreja, V., Manoharan, P., Kaur, A., Kamruzzaman, M. M., Dhaou, I. B., & Iwendi, C. (2022) · Electronics · doi:10.3390/electronics11030495 · Cited by 308
Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion
Hasan, R. I., Yusuf, S. M., & Alzubaidi, L. (2020) · Plants · doi:10.3390/plants9101302 · Cited by 213
Metadata
- Authors
- Ozguven, M. M., & Adem, K.
- Venue
- Physica A: Statistical Mechanics and its Applications
- Publisher
- Elsevier BV
- Published
- 2019-12
- Volume
- 535
- Pages
- 122537
- ISSN
- 0378-4371 (ISSN-L)
- OpenAlex
- W2970815987
- Cited by
- 299
- References
- 23
- FWCI
- 20.83
- 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: Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms. https://app.cochat.ai/papers/10.1016/j.physa.2019.122537
- The paper
- Ozguven, M. M., & Adem, K. (2019). Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms. Physica A: Statistical Mechanics and its Applications. https://doi.org/10.1016/j.physa.2019.122537
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.
