Citation: LU Hao-Xiang, XU Ming-Chang, ZHANG Wei-Dong, YANG Hui-Hua, LIU Zhen-Bing. Identification of Citrus Huanglongbing Based on Contractive Auto-Encoder Combined Extreme Learning Machine[J]. Chinese Journal of Analytical Chemistry, ;2019, 47(5): 652-660. doi: 10.19756/j.issn.0253-3820.181659 shu

Identification of Citrus Huanglongbing Based on Contractive Auto-Encoder Combined Extreme Learning Machine

1.
School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China;
2.
School of Computer and Information Security, Guilin University of Electronic Technology, Guilin 541004, China;
3.
School of Automation, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received Date: 18 October 2018
Revised Date: 18 March 2019

Fund Project: This work was supported by the National Natural Sciences Foundation of China (Nos. 21365008, 61562013) and the Director's Fund of Guangxi Key Laboratory of Automatic Testing Technology and Instruments (No. YQ18108).

To solve the problems of low accuracy and poor reliability of the citrus Huanglongbing identification model, a method of Citrus Huanglongbing identification based on Contractive auto-encoder combined extreme learning machine (CAE-ELM) was proposed. To reduce the running time and improve the stability and classification ability of the model, extreme learning machine was used to replace the Softmax of Contractive auto-encoder top layer and reverse fine-tuning stage. Among them, the Contractive auto-encoding realized the extraction of deep features of the samples, and the extreme learning machine realized the identification. To evaluate the performance of CAE-ELM model, experiments were carried out using near infrared spectroscopy data of citrus leaves with different proportions as training sets. Meanwhile, summation wavelet extreme learning machine (SWELM), ELM, support vector machine (SVM), stacked denoising auto-encoder (SDAE), back propagation (BP) and CAE were used as comparative methods. In the identification experiment of citrus Huanglongbing, CAE-ELM maintained the highest classification accuracy no matter how the training sets change. Especially, when the ratio of training sets and test sets was 1080/165, the classification accuracy reached 100.00. At the same time, CAE-ELM model trained faster than SDAE, CAE and BP, but slower than SVM, ELM and SWELM. The results showed that the CAE-ELM model could be used to accurately identify the citrus Huanglongbing, with good robustness and scalability.
- Contractive auto-encoder,
- Extreme learning machine,
- Near-infrared spectroscopy,
- Citrus Huanglongbing
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