Identification of Xinjiang Jujube Varieties Based on Hyperspectral Technique and Machine Learning

Liu Lixin; He Di; Li Mengzhu; Liu Xing; Qu Junle

doi:10.3788/CJL202047.1111002

Chinese Journal of Lasers, Volume. 47, Issue 11, 1111002(2020)

Identification of Xinjiang Jujube Varieties Based on Hyperspectral Technique and Machine Learning

Liu Lixin^1,2、*, He Di¹, Li Mengzhu¹, Liu Xing³, and Qu Junle⁴

Author Affiliations

¹School of Physics and Optoelectronic Engineering, Xidian University, Xi''an, Shaanxi 710071, China

²State Key Laboratory of Transient Optics and Photonics, Xi''an Institute of Optics and Precision Mechanics of Chinese Academy of Sciences, Xi''an, Shaanxi 710119, China

³Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen, Guangdong 518118, China

⁴College of Physics and Optoelectronic Engineering, Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen University, Shenzhen, Guangdong 518060, China

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Figures & Tables(12)

Fig. 1. Schematic of hyperspectral system

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Fig. 2. Spectral profiles of Jujube samples before and after preprocess. (a) Average spectra; (b) original spectra; (c) preprocessed spectra by MSC; (d) preprocessed spectra by SNV; (e) preprocessed spectra by 1-Der; (f) preprocessed spectra by SG smoothing

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Fig. 3. Extracting characteristic bands by PCA. (a) Scores of the first three principal components; (b) variance contribution rate of the first ten principal components

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Fig. 4. Root-mean-square error calculated according to the number of selected characteristic variables

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Fig. 5. Extracting characteristic bands by CARS. (a) Variation curve of the number of variables with the number of sampling; (b) variation curve of RMSECV with the number of sampling; (c) variation path of variable regression coefficient

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Table 1. Identification results of LDA model with different pretreatment methods
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Table 1. Identification results of LDA model with different pretreatment methods
Pretreatment method Number of misjudgment Overall accuracy /%
Jinsi jujube Jun jujube Tan jujube
Original 37 22 20 65.35
SG smoothing 37 22 20 65.35
MSC 29 26 35 60.53
SNV 29 25 35 60.96
1-Der 29 14 11 76.32

Table 2. Identification results of KNN model with different pretreatment methods
View table
Table 2. Identification results of KNN model with different pretreatment methods
Pretreatment method Number of misjudgment Overall accuracy /%
Jinsi jujube Jun jujube Tan jujube
Original 18 16 5 82.89
SG Smoothing 18 16 5 82.89
MSC 14 12 12 83.33
SNV 14 12 12 83.33
1-Der 0 0 0 100

Table 3. Identification results of SVM model with different pretreatment methods
View table
Table 3. Identification results of SVM model with different pretreatment methods
Pretreatment method Number of misjudgment Overall accuracy /%
Jinsi jujube Jun jujube Tan jujube
Original 5 4 1 95.61
SG Smoothing 3 3 2 96.49
MSC 2 4 2 96.49
SNV 2 4 2 96.49
1-Der 0 0 0 100

Table 4. Identification results of LDA model with different characteristic bands extraction methods
View table
Table 4. Identification results of LDA model with different characteristic bands extraction methods
Characteristic bandsextraction method Number of misjudgment Overall accuracy /%
Jinsi jujube Jun jujube Tan jujube
FS 29 14 11 76.32
PCA 13 20 19 77.19
SPA 18 16 10 80.70
CARS 15 12 6 85.53

Table 5. Identification results of KNN model with different characteristic bands extraction methods
View table
Table 5. Identification results of KNN model with different characteristic bands extraction methods
Characteristic bandsextraction method Number of misjudgment Overall accuracy /%
Jinsi jujube Jun jujube Tan jujube
FS 0 0 0 100
PCA 10 10 9 87.28
SPA 9 11 6 88.60
CARS 0 3 0 98.68

Table 6. Identification results of SVM model with different characteristic bands extraction methods
View table
Table 6. Identification results of SVM model with different characteristic bands extraction methods
Characteristic bandsextraction method Number of misjudgment Overall accuracy /%
Jinsi jujube Jun jujube Tan jujube
FS 0 0 0 100
PCA 8 10 7 89.04
SPA 4 5 3 94.74
CARS 2 2 0 98.25

Table 7. Accuracy and runtime of SVM model based on different characteristic bands extraction methods
View table
Table 7. Accuracy and runtime of SVM model based on different characteristic bands extraction methods
Characteristic bands extraction method Number of characteristic bands Accuracy /% Runtime /s
FS 1356 100 1.497
PCA 10 89.04 0.026
SPA 13 94.74 0.032
CARS 275 98.25 0.167

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Liu Lixin, He Di, Li Mengzhu, Liu Xing, Qu Junle. Identification of Xinjiang Jujube Varieties Based on Hyperspectral Technique and Machine Learning[J]. Chinese Journal of Lasers, 2020, 47(11): 1111002

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Paper Information

Category: spectroscopy

Received: Apr. 16, 2020

Accepted: --

Published Online: Oct. 20, 2020

The Author Email: Lixin Liu (lxliu@xidian.edu.cn)

DOI:10.3788/CJL202047.1111002

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Identification results of LDA model with different pretreatment methods

Table 1. Identification results of LDA model with different pretreatment methods

Table 2. Identification results of KNN model with different pretreatment methods

Table 2. Identification results of KNN model with different pretreatment methods

Table 3. Identification results of SVM model with different pretreatment methods

Table 3. Identification results of SVM model with different pretreatment methods

Table 4. Identification results of LDA model with different characteristic bands extraction methods

Table 4. Identification results of LDA model with different characteristic bands extraction methods

Table 5. Identification results of KNN model with different characteristic bands extraction methods

Table 5. Identification results of KNN model with different characteristic bands extraction methods

Table 6. Identification results of SVM model with different characteristic bands extraction methods

Table 6. Identification results of SVM model with different characteristic bands extraction methods

Table 7. Accuracy and runtime of SVM model based on different characteristic bands extraction methods

Table 7. Accuracy and runtime of SVM model based on different characteristic bands extraction methods