Multi-Input Convolutional Neural Network for Flower Grading
Joint Authors
Sun, Yu
Wang, Guan
Zhu, Lin
Zhao, Fang
Source
Journal of Electrical and Computer Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-08-31
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Abstract EN
Flower grading is a significant task because it is extremely convenient for managing the flowers in greenhouse and market.
With the development of computer vision, flower grading has become an interdisciplinary focus in both botany and computer vision.
A new dataset named BjfuGloxinia contains three quality grades; each grade consists of 107 samples and 321 images.
A multi-input convolutional neural network is designed for large scale flower grading.
Multi-input CNN achieves a satisfactory accuracy of 89.6% on the BjfuGloxinia after data augmentation.
Compared with a single-input CNN, the accuracy of multi-input CNN is increased by 5% on average, demonstrating that multi-input convolutional neural network is a promising model for flower grading.
Although data augmentation contributes to the model, the accuracy is still limited by lack of samples diversity.
Majority of misclassification is derived from the medium class.
The image processing based bud detection is useful for reducing the misclassification, increasing the accuracy of flower grading to approximately 93.9%.
American Psychological Association (APA)
Sun, Yu& Zhu, Lin& Wang, Guan& Zhao, Fang. 2017. Multi-Input Convolutional Neural Network for Flower Grading. Journal of Electrical and Computer Engineering،Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1175441
Modern Language Association (MLA)
Sun, Yu…[et al.]. Multi-Input Convolutional Neural Network for Flower Grading. Journal of Electrical and Computer Engineering No. 2017 (2017), pp.1-8.
https://search.emarefa.net/detail/BIM-1175441
American Medical Association (AMA)
Sun, Yu& Zhu, Lin& Wang, Guan& Zhao, Fang. Multi-Input Convolutional Neural Network for Flower Grading. Journal of Electrical and Computer Engineering. 2017. Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1175441
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1175441