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Tomato crop disease classification using pre-trained deep learning algorithm
Open accessAbstract
The wide scale prevalence of diseases in tomato crop affects the production quality and quantity. In order to counteract the problem early diagnosis of diseases using a fast reliable nondestructive method will benefit the farmers. In this study images of tomato leaves (6 diseases and a healthy class) obtained from PlantVillage dataset is provided as input to two deep learning based architectures namely AlexNet and VGG16 net. The role of number of images and significance of hyperparameters namely minibatch size, weight and bias learning rate in the classification accuracy and execution time have been analyzed.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Cited by
Ayoub Shaikh, T., Rasool, T., & Rasheed Lone, F. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.107119 · Cited by 768
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
Deep Learning-Based Leaf Disease Detection in Crops Using Images for Agricultural Applications
J., A., Eunice, J., Popescu, D. E., Chowdary, M. K., & Hemanth, J. (2022) · Agronomy · doi:10.3390/agronomy12102395 · Cited by 409
Development of Efficient CNN model for Tomato crop disease identification
Agarwal, M., Gupta, S. K., & Biswas, K. K. (2020) · Sustainable Computing: Informatics and Systems · doi:10.1016/j.suscom.2020.100407 · Cited by 331
Ozguven, M. M., & Adem, K. (2019) · Physica A: Statistical Mechanics and its Applications · doi:10.1016/j.physa.2019.122537 · Cited by 299
Metadata
- Authors
- Rangarajan, A. K., Purushothaman, R., & Ramesh, A.
- Venue
- Procedia Computer Science
- Publisher
- Elsevier BV
- Published
- 2018
- Volume
- 133
- Pages
- 1040-1047
- ISSN
- 1877-0509 (ISSN-L)
- OpenAlex
- W2884416373
- Cited by
- 617
- References
- 1
- FWCI
- 109.45
- Type
- conference paper
- Language
- en
- Open access
- diamond
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