DIAGNOSIS OF CITRUS GREENING USING RAMAN SPECTROSCOPY-BASED PATTERN RECOGNITION
Abstract
This study verified the applicability of Raman spectroscopy for the detection and classification of disease in citrus leaves. The Raman spectra of citrus leaves were collected using a SENTERRA confocal microprobe Raman spectrometer and divided into five types, Huanglongbing (HLB), moderate HLB, serious HLB, nutrient deficiency, and normal. The backgrounds of the spectra were deducted by different methods, and partial least squares discrimination analysis (PLS-DA) and extreme learning machine (ELM) were used to build the mathematical model. At the same time, the data dimension was reduced using principal component analysis (PCA) and successive projection algorithm (SPA) in order to optimize and improve the classification accuracy of the model. The experiments showed that the predictive ability of the PLS-DA model with 1850 input variables by 2 times polynomial fitting deducted backgrounds was better, the recognition correct rate being 100%. The results show that Raman spectroscopy has potential for rapid diagnosis of citrus HLB.
Keywords
citrus Huanglongbing,
Raman spectroscopy,
partial least squares discrimination analysis,
extreme learning machine,
successive projection algorithm
Supporting agencies: The research was funded by the National Natural Science Foundation of China (No. 31760344), the Center of the Technology and Equipment of the Intelligent Management for the Southern Mountain Orchard Collaborative Innovation (No. 2014-60), the Jiangxi Advantage Science and Technology Innovation Team Construction Project (No. 20153BCB24002), the Special../anzhuang/youdao/Dict/7.5.2.0/resultui/dict/?keyword= Project of Innovation Ability for Jiangxi Province (No. [2016]1074), the Photoelectric Inspection Engineering Technology Research Center of Jiangxi Province (No. 20161BCD40021), and the Youth Project of Jiangxi Province Education Department (No. GJJ160517).
About the Authors
Y. Liu
College of Mechanical and Vehicle Engineering, East China Jiaotong University
China
Jiangxi Province
China
Jiangxi Province
H. Xiao
College of Mechanical and Vehicle Engineering, East China Jiaotong University
China
Jiangxi Province
China
Jiangxi Province
Y. Hao
College of Mechanical and Vehicle Engineering, East China Jiaotong University
China
Jiangxi Province
China
Jiangxi Province
L. Ye
College of Mechanical and Vehicle Engineering, East China Jiaotong University
China
Jiangxi Province
China
Jiangxi Province
X. Jiang
College of Mechanical and Vehicle Engineering, East China Jiaotong University
China
Jiangxi Province
China
Jiangxi Province
H. Wang
College of Mechanical and Vehicle Engineering, East China Jiaotong University
China
Jiangxi Province
China
Jiangxi Province
X. Sun
College of Mechanical and Vehicle Engineering, East China Jiaotong University
China
Jiangxi Province
China
Jiangxi Province
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Review
For citations:
Liu Y., Xiao H., Hao Y., Ye L., Jiang X., Wang H., Sun X. DIAGNOSIS OF CITRUS GREENING USING RAMAN SPECTROSCOPY-BASED PATTERN RECOGNITION. Zhurnal Prikladnoii Spektroskopii. 2020;87(1):170(1)-170(9).
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