The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Plant Disease Detection and Classification by Deep Learning
Open accessAbstract
Plant diseases affect the growth of their respective species, therefore their early identification is very important. Many Machine Learning (ML) models have been employed for the detection and classification of plant diseases but, after the advancements in a subset of ML, that is, Deep Learning (DL), this area of research appears to have great potential in terms of increased accuracy. Many developed/modified DL architectures are implemented along with several visualization techniques to detect and classify the symptoms of plant diseases. Moreover, several performance metrics are used for the evaluation of these architectures/techniques. This review provides a comprehensive explanation of DL models used to visualize various plant diseases. In addition, some research gaps are identified from which to obtain greater transparency for detecting diseases in plants, even before their symptoms appear clearly.
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Artificial intelligence: A powerful paradigm for scientific research
Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., Liu, X., Wu, Y., Dong, F., Qiu, C.-W., et al. (2021) · The Innovation · doi:10.1016/j.xinn.2021.100179 · Cited by 1,726
Machine learning in agriculture domain: A state-of-art survey
Meshram, V., Patil, K., Meshram, V., Hanchate, D., & Ramkteke, S. D. (2021) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2021.100010 · Cited by 367
Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning
Nguyen, C., Sagan, V., Maimaitiyiming, M., Maimaitijiang, M., Bhadra, S., & Kwasniewski, M. T. (2021) · Sensors · doi:10.3390/s21030742 · Cited by 281
Pham, T. N., Tran, L. V., & Dao, S. V. T. (2020) · IEEE Access · doi:10.1109/access.2020.3031914 · Cited by 262
“[4] conducted a review of plant disease detection and classification by Deep Learning techniques.”
MethodSaleem, M. H., Potgieter, J., & Arif, K. M. (2020) · Plants · doi:10.3390/plants9101319 · Cited by 259
Self-citation
Metadata
- Authors
- Saleem, M. H., Potgieter, J., & Arif, K. M.
- Venue
- Plants
- Publisher
- MDPI AG
- Published
- 2019-10-31
- Volume
- 8
- Issue
- 11
- Pages
- 468
- ISSN
- 2223-7747 (ISSN-L)
- OpenAlex
- W2982381523
- Cited by
- 886
- References
- 10
- FWCI
- 42.17
- Type
- journal article
- Language
- en
- Open access
- gold
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