The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Deep Learning for Tomato Diseases: Classification and Symptoms Visualization
Open accessAbstract
Several studies have invested in machine learning classifiers to protect plants from diseases by processing leaf images. Most of the proposed classifiers are trained and evaluated with small datasets, focusing on the extraction of hand-crafted features from image to classify the leaves. In this study, we have used a large dataset compared to the state-of-the art. Here, the dataset contains 14,828 images of tomato leaves infected with nine diseases. To train our classifier, we have introduced the Convolutional Neural Network (CNN) as a learning algorithm. One of the biggest advantages of CNN is the automatic extraction of features by processing directly the raw images. To analyze the proposed deep model, we have used visualization methods to understand symptoms and to localize disease regions in leaf. The obtained results are encouraging, reaching 99.18% of accuracy, which ourperforms dramatically shallow models, and they can be used as a practical tool for farmers to protect tomato against disease.
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Cites
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Going deeper with convolutions
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Understanding Neural Networks Through Deep Visualization
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(2014) · arXiv (Cornell University) · Cited by 942
Fast and Accurate Detection and Classification of Plant Diseases
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Automatic Detection of Diseased Tomato Plants Using Thermal and Stereo Visible Light Images
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“Diagnosis and classification of grape leaf diseases using neural networks”
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Cited by
Plant disease identification from individual lesions and spots using deep learning
Arnal Barbedo, J. G. (2019) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2019.02.002 · Cited by 783
Barbedo, J. G. A. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.08.013 · Cited by 750
ToLeD: Tomato Leaf Disease Detection using Convolution Neural Network
Agarwal, M., Singh, A., Arjaria, S., Sinha, A., & Gupta, S. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.03.225 · Cited by 697
Factors influencing the use of deep learning for plant disease recognition
Barbedo, J. G. A. (2018) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2018.05.013 · Cited by 649
A review of computer vision technologies for plant phenotyping
Li, Z., Guo, R., Li, M., Chen, Y., & Li, G. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105672 · Cited by 391
Metadata
- Authors
- Brahimi, M., Boukhalfa, K., & Moussaoui, A.
- Venue
- Applied Artificial Intelligence
- Publisher
- Informa UK Limited
- Published
- 2017-04-21
- Volume
- 31
- Issue
- 4
- Pages
- 299-315
- ISSN
- 0883-9514 (ISSN-L)
- 1087-6545
- OpenAlex
- W2614850301
- Cited by
- 958
- References
- 17
- FWCI
- 33.79
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Brahimi, M., Boukhalfa, K., & Moussaoui, A. (2017). Deep Learning for Tomato Diseases: Classification and Symptoms Visualization. Applied Artificial Intelligence. https://doi.org/10.1080/08839514.2017.1315516
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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.
