The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
ToLeD: Tomato Leaf Disease Detection using Convolution Neural Network
Open accessAbstract
Tomato is the most popular crop in the world and in every kitchen, it is found in different forms irrespective of the cuisine. After potato and sweet potato, it is the crop which is cultivated worldwide. India ranked 2 in the production of tomato. However, the quality and quantity of tomato crop goes down due to the various kinds of diseases. So, to detect the disease a deep learning-based approach is discussed in the article. For the disease detection and classification, a Convolution Neural Network based approach is applied. In this model, there are 3 convolution and 3 max pooling layers followed by 2 fully connected layer. The experimental results shows the efficacy of the proposed model over pre-trained model i.e. VGG16, InceptionV3 and MobileNet. The classification accuracy varies from 76% to 100% with respect to classes and average accuracy of the proposed model is 91.2% for the 9 disease and 1 healthy class.
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Cites
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Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Rethinking the Inception Architecture for Computer Vision
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MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
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Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077
Using Deep Learning for Image-Based Plant Disease Detection
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Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
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A comparative study of fine-tuning deep learning models for plant disease identification
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Deep Learning for Tomato Diseases: Classification and Symptoms Visualization
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Tomato crop disease classification using pre-trained deep learning algorithm
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Cited by
Automatic and Reliable Leaf Disease Detection Using Deep Learning Techniques
Chowdhury, M. E. H., Rahman, T., Khandakar, A., Ayari, M. A., Khan, A. U., Khan, M. S., Al-Emadi, N., Reaz, M. B. I., Islam, M. T., & Ali, S. H. M. (2021) · AgriEngineering · doi:10.3390/agriengineering3020020 · Cited by 401
Metadata
- Authors
- Agarwal, M., Singh, A., Arjaria, S., Sinha, A., & Gupta, S.
- Venue
- Procedia Computer Science
- Publisher
- Elsevier BV
- Published
- 2020
- Volume
- 167
- Pages
- 293-301
- ISSN
- 1877-0509 (ISSN-L)
- OpenAlex
- W3016522735
- Cited by
- 697
- References
- 19
- FWCI
- 111.27
- Type
- conference paper
- Language
- en
- Open access
- diamond
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