The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Digital image processing techniques for detecting, quantifying and classifying plant diseases
Open accessAbstract
This paper presents a survey on methods that use digital image processing techniques to detect, quantify and classify plant diseases from digital images in the visible spectrum. Although disease symptoms can manifest in any part of the plant, only methods that explore visible symptoms in leaves and stems were considered. This was done for two main reasons: to limit the length of the paper and because methods dealing with roots, seeds and fruits have some peculiarities that would warrant a specific survey. The selected proposals are divided into three classes according to their objective: detection, severity quantification, and classification. Each of those classes, in turn, are subdivided according to the main technical solution used in the algorithm. This paper is expected to be useful to researchers working both on vegetable pathology and pattern recognition, providing a comprehensive and accessible overview of this important field of research.
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Cites
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Mahlein, A.-K., Oerke, E.-C., Steiner, U., & Dehne, H.-W. (2012) · European Journal of Plant Pathology · doi:10.1007/s10658-011-9878-z · Cited by 579
Competitive learning with floating-gate circuits
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Picture Processing and Psychopictorics
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Cited by
Barbedo, J. G. A. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.08.013 · Cited by 750
Disease detection on the leaves of the tomato plants by using deep learning
Durmus, H., Gunes, E. O., & Kirci, M. (2017) · 2017 6th International Conference on Agro-Geoinformatics · doi:10.1109/agro-geoinformatics.2017.8047016 · Cited by 544
Khan, A., Vibhute, A. D., Mali, S., & Patil, C. H. (2022) · Ecological Informatics · doi:10.1016/j.ecoinf.2022.101678 · Cited by 382
Revolutionizing Visuals: The Role of Generative AI in Modern Image Generation
Bansal, G., Nawal, A., Chamola, V., & Herencsar, N. (2024) · ACM Transactions on Multimedia Computing, Communications, and Applications · doi:10.1145/3689641 · Cited by 91
Mishra, J., Tiwari, M., Singh, S. T., & Goswami, S. (2022) · 2021 4th International Conference on Recent Trends in Computer Science and Technology (ICRTCST) · doi:10.1109/icrtcst54752.2022.9782009 · Cited by 4
Metadata
- Authors
- Arnal Barbedo, J. G.
- Venue
- SpringerPlus
- Published
- 2013-12
- Volume
- 2
- Issue
- 1
- Pages
- 660-660
- ISSN
- 2193-1801 (ISSN-L)
- OpenAlex
- W2090813402
- Cited by
- 623
- References
- 20
- FWCI
- 17.58
- Type
- journal article
- Language
- en
- Open access
- diamond
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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.
