QUALITY GRADING OF CROWN FLOWERS USING CONVOLUTIONAL NEURAL NETWORK
- Publication Type:
- Journal Article
- Citation:
- ICIC Express Letters, 2023, 17, (2), pp. 143-152
- Issue Date:
- 2023-02-01
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| el-17-02-02.pdf | Published version | 1.58 MB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
This article presents an image classification for quality grading of crown flowers using convolutional neural network and transfer learning. It consists of 1) image acquisition, 2) pre-process, and 3) data classification. Using data of 1,500 white crown flowers, they are divided into five classes as follows: 1) flowers with the base of the petals close together, 2) flower with mold, 3) flowers with more than one bad trait, 4) flowers with unequal petal length, and 5) complete flower. The experiment divided the data into two parts, including 80% of training data and 20% of testing data. This article experiments with four models, including Convolutional Neural Network (CNN), Xception, VGG16, and InceptionV3. The experiment results show that accuracies of convolutional neural networks and InceptionV3 Networks were 85%; moreover, those of Xception and VGG16 were 83% and 81%, respectively. Finally, the performance measurements of the CNN model show the highest average of all precision, recall, and F1-score equations. Accordingly, the CNN model is the deep learning of the characteristics of the image, which gets to more efficiently and accurately than other models. It can be applied to classifying the quality of crown flowers, which supports the gardeners in grading flowers more efficiently.
Please use this identifier to cite or link to this item: