Paper Info
Title
Convolutional Recurrent Neural Networks for Observation-Centered Plant Identification.
Abstract
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.
Year
DOI
Venue
2018
10.1155/2018/9373210
JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING
Field
DocType
Volume
Overhead (computing),Pattern recognition,Convolutional neural network,Computer science,Recurrent neural network,Electronic engineering,Extractor,Human behavior,Artificial intelligence,Classification rate,Plant identification,Test set
Journal
2018
ISSN
Citations 
PageRank 
2090-0147
1
0.37
References 
Authors
5
5
Name
Order
Citations
PageRank
Xuanxin Liu110.71
Fu Xu221.74
Yu Sun3223.15
Haiyan Zhang412.40
Zhibo Chen530644.72