Classification of <i>Ocimum basilicum</i> Using a Convolutional Neural Network
Basil varieties were classified using a convolutional neural network (CNN) with VGG16 architecture. The developed system in this study identified and classified the variety of basil images. The system applied the contrast-limited adaptive histogram equalization (CLAHE) algorithm to the basil image i...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/92/1/54 |
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| Summary: | Basil varieties were classified using a convolutional neural network (CNN) with VGG16 architecture. The developed system in this study identified and classified the variety of basil images. The system applied the contrast-limited adaptive histogram equalization (CLAHE) algorithm to the basil image in the architecture VGG16 to extract features and classify the images. The system was tested using 50 images, and the confusion matrix showed an 82.00% accuracy. An inaccurate output was caused by the wrong positions or the size of the leaf. Of the 50 basil images, 41 were correctly classified. Two models were created in the study for epochs of eight and 10. The best model study was chosen based on accuracy. The best model showed an accuracy of 97% in training for 10 epochs. |
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| ISSN: | 2673-4591 |