The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification
Open accessAbstract
The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%.
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Cites
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Cited by
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 Learning for IoT Big Data and Streaming Analytics: A Survey
Mohammadi, M., Al-Fuqaha, A., Sorour, S., & Guizani, M. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2844341 · Cited by 1,471
Ball, J. E., Anderson, D. T., & Chan, C. S. (2017) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.11.042609 · Cited by 746
The digitization of agricultural industry – a systematic literature review on agriculture 4.0
Abbasi, R., Martinez, P., & Ahmad, R. (2022) · Smart Agricultural Technology · doi:10.1016/j.atech.2022.100042 · Cited by 531
Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case
Johannes, A., Picon, A., Alvarez-Gila, A., Echazarra, J., Rodriguez-Vaamonde, S., Navajas, A. D., & Ortiz-Barredo, A. (2017) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2017.04.013 · Cited by 426
Metadata
- Authors
- Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., & Stefanovic, D.
- Venue
- Computational Intelligence and Neuroscience
- Publisher
- Wiley
- Published
- 2016
- Volume
- 2016
- Pages
- 1-11
- ISSN
- 1687-5265 (ISSN-L)
- 1687-5273
- OpenAlex
- W2470368200
- Cited by
- 2,029
- References
- 7
- FWCI
- 93.70
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., & Stefanovic, D. (2016). Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification. Computational Intelligence and Neuroscience. https://doi.org/10.1155/2016/3289801
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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.
