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Convolutional Recurrent Neural Networks for Observation-Centered Plant Identification
Joint Authors
Sun, Yu
Zhang, Haiyan
Xu, Fu
Chen, Zhibo
Liu, Xuanxin
Source
Journal of Electrical and Computer Engineering
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-03-08
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Information Technology and Computer Science
Abstract EN
Traditional image-centered methods of plant identification could be confused due to various views, uneven illuminations, and growth cycles.
To tolerate the significant intraclass variances, the convolutional recurrent neural networks (C-RNNs) are proposed for observation-centered plant identification to mimic human behaviors.
The C-RNN model is composed of two components: the convolutional neural network (CNN) backbone is used as a feature extractor for images, and the recurrent neural network (RNN) units are built to synthesize multiview features from each image for final prediction.
Extensive experiments are conducted to explore the best combination of CNN and RNN.
All models are trained end-to-end with 1 to 3 plant images of the same observation by truncated back propagation through time.
The experiments demonstrate that the combination of MobileNet and Gated Recurrent Unit (GRU) is the best trade-off of classification accuracy and computational overhead on the Flavia dataset.
On the holdout test set, the mean 10-fold accuracy with 1, 2, and 3 input leaves reached 99.53%, 100.00%, and 100.00%, respectively.
On the BJFU100 dataset, the C-RNN model achieves the classification rate of 99.65% by two-stage end-to-end training.
The observation-centered method based on the C-RNNs shows potential to further improve plant identification accuracy.
American Psychological Association (APA)
Liu, Xuanxin& Xu, Fu& Sun, Yu& Zhang, Haiyan& Chen, Zhibo. 2018. Convolutional Recurrent Neural Networks for Observation-Centered Plant Identification. Journal of Electrical and Computer Engineering،Vol. 2018, no. 2018, pp.1-7.
https://search.emarefa.net/detail/BIM-1184567
Modern Language Association (MLA)
Liu, Xuanxin…[et al.]. Convolutional Recurrent Neural Networks for Observation-Centered Plant Identification. Journal of Electrical and Computer Engineering No. 2018 (2018), pp.1-7.
https://search.emarefa.net/detail/BIM-1184567
American Medical Association (AMA)
Liu, Xuanxin& Xu, Fu& Sun, Yu& Zhang, Haiyan& Chen, Zhibo. Convolutional Recurrent Neural Networks for Observation-Centered Plant Identification. Journal of Electrical and Computer Engineering. 2018. Vol. 2018, no. 2018, pp.1-7.
https://search.emarefa.net/detail/BIM-1184567
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1184567