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Journal of Electrical and Computer Engineering
Volume 2017, Article ID 9240407, 8 pages
https://doi.org/10.1155/2017/9240407
Research Article

Multi-Input Convolutional Neural Network for Flower Grading

School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China

Correspondence should be addressed to Fang Zhao; nc.ude.ufjb@oahzgnaf

Received 21 April 2017; Revised 11 July 2017; Accepted 12 July 2017; Published 31 August 2017

Academic Editor: Sos Agaian

Copyright © 2017 Yu Sun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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%.