Prof. Dongyang Zhang Profile

Prof. Dongyang Zhang

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Proceedings Article | 8 March 2017 Paper

Fast quantitative detection of thiram using surface-enhanced Raman scattering and support vector machine regression

Shizhuang Weng, Baohong Yuan, Zede Zhu, Linsheng Huang, Dongyan Zhang, Ling Zheng

Proceedings Volume 10255, 102553B (2017) https://doi.org/10.1117/12.2267123

KEYWORDS: Raman scattering, Surface enhanced Raman spectroscopy, Principal component analysis, Particle swarm optimization, Data modeling, Optimization (mathematics), Analytical research, Quantitative analysis, Feature extraction, Particles

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As a novel and ultrasensitive detection technology that had advantages of fingerprint effect, high speed and low cost, surface-enhanced Raman scattering (SERS) was used to develop the regression models for the fast quantitative detection of thiram by support vector machine regression (SVR) in the paper. Meanwhile, three parameter optimization methods, which were grid search (GS), genetic algorithm (GA) and particle swarm optimization (PSO), were employed to optimize the internal parameters of SVR. Furthermore, the influence of the spectral number, spectral wavenumber range and principal component analysis (PCA) on the quantitative detection was also discussed. Firstly, the experiments demonstrate the proposed method can realize the fast and quantitative detection of thiram, and the best result is obtained by GS-SVR with the spectra of the range of characteristic peak which are processed by PCA. And the effect of GS, GA, PSO on the parameter optimization is similar, but the analysis time has a great difference in which GS is the fastest. Considering the analysis accuracy and time simultaneously, the spectral number of samples over each concentration should be set to 50. Then, developing the quantitative model with the spectra of range of characteristic peak can reduce analysis time on the promise of ensuring the detection accuracy. Additionally, PCA can further reduce the detection error through reserving the main information of the spectra data and eliminating the noise.

Proceedings Article | 19 October 2016 Paper

A system for diagnosis of wheat leaf diseases based on Android smartphone

Xinhua Xie, Xiangqian Zhang, Bing He, Dong Liang, Dongyang Zhang, Linsheng Huang

Proceedings Volume 10155, 1015526 (2016) https://doi.org/10.1117/12.2246919

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Owing to the shortages of inconvenience, expensive and high professional requirements etc. for conventional recognition devices of wheat leaf diseases, it does not satisfy the requirements of uploading and releasing timely investigation data in the large-scale field, which may influence the effectiveness of prevention and control for wheat diseases. In this study, a fast, accurate, and robust diagnose system of wheat leaf diseases based on android smartphone was developed, which comprises of two parts—the client and the server. The functions of the client include image acquisition, GPS positioning, corresponding, and knowledge base of disease prevention and control. The server includes image processing, feature extraction, and selection, and classifier establishing. The recognition process of the system goes as follow: when disease images were collected in fields and sent to the server by android smartphone, and then image processing of disease spots was carried out by the server. Eighteen larger weight features were selected by algorithm relief-F and as the input of Relevance Vector Machine (RVM), and the automatic identification of wheat stripe rust and powdery mildew was realized. The experimental results showed that the average recognition rate and predicted speed of RVM model were 5.56% and 7.41 times higher than that of Support Vector Machine (SVM). And application discovered that it needs about 1 minute to get the identification result. Therefore, it can be concluded that the system could be used to recognize wheat diseases and real-time investigate in fields.

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