Abstract
In this study, we trained a convolutional neural network to detect disease in tomato leaves and then examined the effect of hyperparameters and layers used when training a convolutional neural network on the trained model. In our study, it was observed that with hyperparameter tuning, it is possible to increase the validation accuracy of a CNN trained from scratch using the plantvillage dataset from 92% to 98%.
| Original language | English |
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| Title of host publication | 2021 13th International Conference on Electrical and Electronics Engineering, ELECO 2021 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 373-377 |
| Number of pages | 5 |
| ISBN (Electronic) | 9786050114379 |
| DOIs | |
| Publication status | Published - 2021 |
| Event | 13th International Conference on Electrical and Electronics Engineering, ELECO 2021 - Virtual, Bursa, Turkey Duration: 25 Nov 2021 → 27 Nov 2021 |
Publication series
| Name | 2021 13th International Conference on Electrical and Electronics Engineering, ELECO 2021 |
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Conference
| Conference | 13th International Conference on Electrical and Electronics Engineering, ELECO 2021 |
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| Country/Territory | Turkey |
| City | Virtual, Bursa |
| Period | 25/11/21 → 27/11/21 |
Bibliographical note
Publisher Copyright:© 2021 Chamber of Turkish Electrical Engineers.