A VGG-19 Model with Transfer Learning and Image Segmentation for Classification of Tomato Leaf Disease
Tomato leaves can have different diseases which can affect harvest performance. Therefore, accurate classification for the early detection of disease for treatment is very important. This article proposes one classification model, in which 16,010 tomato leaf images obtained from the Plant Village da...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | AgriEngineering |
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| Online Access: | https://www.mdpi.com/2624-7402/4/4/56 |
| _version_ | 1797461916217507840 |
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| author | Thanh-Hai Nguyen Thanh-Nghia Nguyen Ba-Viet Ngo |
| author_facet | Thanh-Hai Nguyen Thanh-Nghia Nguyen Ba-Viet Ngo |
| author_sort | Thanh-Hai Nguyen |
| collection | DOAJ |
| description | Tomato leaves can have different diseases which can affect harvest performance. Therefore, accurate classification for the early detection of disease for treatment is very important. This article proposes one classification model, in which 16,010 tomato leaf images obtained from the Plant Village database are segmented before being used to train a deep convolutional neural network (DCNN). This means that this classification model will reduce training time compared with that of the model without segmenting the images. In particular, we applied a VGG-19 model with transfer learning for re-training in later layers. In addition, the parameters such as epoch and learning rate were chosen to be suitable for increasing classification performance. One highlight point is that the leaf images were segmented for extracting the original regions and removing the backgrounds to be black using a hue, saturation, and value (HSV) color space. The segmentation of the leaf images is to synchronize the black background of all leaf images. It is obvious that this segmentation saves time for training the DCNN and also increases the classification performance. This approach improves the model accuracy to 99.72% and decreases the training time of the 16,010 tomato leaf images. The results illustrate that the model is effective and can be developed for more complex image datasets. |
| first_indexed | 2024-03-09T17:26:12Z |
| format | Article |
| id | doaj.art-a4666f6a115146a3aa3c8bcd08d145e0 |
| institution | Directory Open Access Journal |
| issn | 2624-7402 |
| language | English |
| last_indexed | 2024-03-09T17:26:12Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AgriEngineering |
| spelling | doaj.art-a4666f6a115146a3aa3c8bcd08d145e02023-11-24T12:42:48ZengMDPI AGAgriEngineering2624-74022022-10-014487188710.3390/agriengineering4040056A VGG-19 Model with Transfer Learning and Image Segmentation for Classification of Tomato Leaf DiseaseThanh-Hai Nguyen0Thanh-Nghia Nguyen1Ba-Viet Ngo2Faculty of Electrical-Electronic Engineering, Ho Chi Minh City University of Technology and Education, Ho Chi Minh City 700000, VietnamFaculty of Electrical-Electronic Engineering, Ho Chi Minh City University of Technology and Education, Ho Chi Minh City 700000, VietnamFaculty of Electrical-Electronic Engineering, Ho Chi Minh City University of Technology and Education, Ho Chi Minh City 700000, VietnamTomato leaves can have different diseases which can affect harvest performance. Therefore, accurate classification for the early detection of disease for treatment is very important. This article proposes one classification model, in which 16,010 tomato leaf images obtained from the Plant Village database are segmented before being used to train a deep convolutional neural network (DCNN). This means that this classification model will reduce training time compared with that of the model without segmenting the images. In particular, we applied a VGG-19 model with transfer learning for re-training in later layers. In addition, the parameters such as epoch and learning rate were chosen to be suitable for increasing classification performance. One highlight point is that the leaf images were segmented for extracting the original regions and removing the backgrounds to be black using a hue, saturation, and value (HSV) color space. The segmentation of the leaf images is to synchronize the black background of all leaf images. It is obvious that this segmentation saves time for training the DCNN and also increases the classification performance. This approach improves the model accuracy to 99.72% and decreases the training time of the 16,010 tomato leaf images. The results illustrate that the model is effective and can be developed for more complex image datasets.https://www.mdpi.com/2624-7402/4/4/56VGG-19 modeltomato leaf imagestransfer learningimage segmentationHSV color space |
| spellingShingle | Thanh-Hai Nguyen Thanh-Nghia Nguyen Ba-Viet Ngo A VGG-19 Model with Transfer Learning and Image Segmentation for Classification of Tomato Leaf Disease AgriEngineering VGG-19 model tomato leaf images transfer learning image segmentation HSV color space |
| title | A VGG-19 Model with Transfer Learning and Image Segmentation for Classification of Tomato Leaf Disease |
| title_full | A VGG-19 Model with Transfer Learning and Image Segmentation for Classification of Tomato Leaf Disease |
| title_fullStr | A VGG-19 Model with Transfer Learning and Image Segmentation for Classification of Tomato Leaf Disease |
| title_full_unstemmed | A VGG-19 Model with Transfer Learning and Image Segmentation for Classification of Tomato Leaf Disease |
| title_short | A VGG-19 Model with Transfer Learning and Image Segmentation for Classification of Tomato Leaf Disease |
| title_sort | vgg 19 model with transfer learning and image segmentation for classification of tomato leaf disease |
| topic | VGG-19 model tomato leaf images transfer learning image segmentation HSV color space |
| url | https://www.mdpi.com/2624-7402/4/4/56 |
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