Convolutional Recurrent Neural Networks for Observation-Centered Plant Identification

Xuanxin Liu,Zhibo Chen,Yu Sun,Haiyan Zhang,Fu Xu

doi:10.1155/2018/9373210

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.

Highlights

With the development of computer technology, there is increasing interest in the image-based identification of plant species
The convolutional recurrent neural networks (C-recurrent neural network (RNN)) model can be trained end-to-end and the identification error is minimized by stochastic gradient descent with truncated back propagation through time (BPTT)
Extensive experiments are conducted on the leaves observation-centered dataset with the combinations of different convolutional neural network (CNN) backbones and RNN units to show a significant performance improvement compared with traditional transfer learning

Summary

Introduction

With the development of computer technology, there is increasing interest in the image-based identification of plant species. En and Hu [6], Liu and Kan [7], and Zheng et al [8] used shape and texture features of leaves to identify the plants with the base classifier, deep belief network (DBN) classifier, and SVM classifier, respectively. Wang et al [9] extracted a series of color, shape, and texture features from 50 foliage samples, and the accuracy identified by SVM classifier was 91.41%. Ghazi et al [15] used three deep learning networks, that is, GoogLeNet, AlexNet, and VGGNet, to identify species on the LifeCLEF 2015 dataset and the overall accuracy of the best model was 80%. The recognition accuracy on the Flavia and BJFU100 datasets is further improved by the C-RNN models

The Proposed Method

Experiment and Results

Conclusions