Data Augmentation using Adversarial Networks for Tea Diseases Detection
| Main Authors: | Yuwana, R. Sandra, Fauziah, Fani, Heryana, Ana, Krisnandi, Dikdik, Kusumo, R. Budiarianto Suryo, Pardede, Hilman F. |
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| Other Authors: | The Ministry of Research, Technology and Higher Education of the Republic of Indonesia, Research Center for Informatics, Indonesian Institute of Sciences (LIPI), High Performance Computing (HPC) facilities in Research Center for Informatics - LIPI |
| Format: | Article info application/pdf Journal |
| Bahasa: | eng |
| Terbitan: |
Indonesian Institute of Sciences
, 2020
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| Subjects: | |
| Online Access: |
https://www.jurnalet.com/jet/article/view/365 https://www.jurnalet.com/jet/article/view/365/307 https://www.jurnalet.com/jet/article/downloadSuppFile/365/221 https://www.jurnalet.com/jet/article/downloadSuppFile/365/223 |
Daftar Isi:
- Deep learning technology has a better result when trained using an abundant amount of data. However, collecting such data is expensive and time consuming. On the other hand, limited data often be the inevitable condition. To increase the number of data, data augmentation is usually implemented. By using it, the original data are transformed, by rotating, shifting, or both, to generate new data artificially. In this paper, generative adversarial networks (GAN) and deep convolutional GAN (DCGAN) are used for data augmentation. Both approaches are applied for diseases detection. The performance of the tea diseases detection on the augmented data is evaluated using various deep convolutional neural network (DCNN) including AlexNet, DenseNet, ResNet, and Xception. The experimental results indicate that the highest GAN accuracy is obtained by DenseNet architecture, which is 88.84%, baselines accuracy on the same architecture is 86.30%. The results of DCGAN accuracy on the use of the same architecture show a similar trend, which is 88.86%.