Plant Species Identification by Bi-channel Deep Convolutional Networks

Guiqing He, Zhaoqiang Xia, Qiqi Zhang, Haixi Zhang, Jianping Fan

科研成果: 期刊稿件会议文章同行评审

8 引用 (Scopus)

摘要

Plant species identification achieves much attention recently as it has potential application in the environmental protection and human life. Although deep learning techniques can be directly applied for plant species identification, it still needs to be designed for this specific task to obtain the state-of-art performance. In this paper, a bi-channel deep learning framework is developed for identifying plant species. In the framework, two different sub-networks are fine-tuned over their pretrained models respectively. And then a stacking layer is used to fuse the output of two different sub-networks. We construct a plant dataset of Orchidaceae family for algorithm evaluation. Our experimental results have demonstrated that our bi-channel deep network can achieve very competitive performance on accuracy rates compared to the existing deep learning algorithm.

源语言英语
文章编号012015
期刊Journal of Physics: Conference Series
1004
1
DOI
出版状态已出版 - 25 4月 2018
活动2nd International Conference on Machine Vision and Information Technology, CMVIT 2018 - Hong Kong, 中国
期限: 23 2月 201825 2月 2018

指纹

探究 'Plant Species Identification by Bi-channel Deep Convolutional Networks' 的科研主题。它们共同构成独一无二的指纹。

引用此