Near-Infrared Spectral Waveband Selection for Soil Potassium Content Based on Simulated Annealing

Chunting Li; Youyou Zhang; Huazhou Chen; Jie Gu; Lina Mo; Xiaoyu Li

doi:10.3788/LOP202259.1330002

Laser & Optoelectronics Progress, Volume. 59, Issue 13, 1330002(2022)

Near-Infrared Spectral Waveband Selection for Soil Potassium Content Based on Simulated Annealing

Chunting Li¹, Youyou Zhang¹, Huazhou Chen^1、*, Jie Gu², Lina Mo³, and Xiaoyu Li¹

Author Affiliations

¹College of Science, Guilin University of Technology, Guilin 541004, Guangxi , China

²School of Electrical and Information Engineering, Chongqing College of Humanities, Science & Technology, Chongqing 401524, China

³School of Tourism Data, Guilin Tourism University, Guilin 541006, Guangxi , China

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Figures & Tables(8)

Fig. 1. NIR spectra of 135 soil samples. (a) Raw spectra; (b) spectra obtained after preprocessing

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Fig. 2. $R_{M S E}^{V}$ corresponding to the optimal feature waveband of different models

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Fig. 3. Iterative optimization process of the SA-iPLS model. (a) N=60; (b) N=70; (c) N=80; (d) N=90; (e) N=100

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Fig. 4. Optimal feature waveband selected by SA-iPLS model (N=90)

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Fig. 5. Prediction results of SA-iPLS for the test samples

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Table 1. Mass fraction of potassium element in the sample set
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Table 1. Mass fraction of potassium element in the sample set
Sample set Number of samples Maximum Minimum /% Average /% Standard deviation /%
Calibration set 115 0.192 0.053 0.108 0.032
Test set 20 0.162 0.065 0.101 0.023

Table 2. Optimal characteristic bands screened by different models

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Table 2. Optimal characteristic bands screened by different models

Model

Number of subintervals

Number of selected subintervals

Selected wavebands /cm^-1

iPLS

8036-7980

SiPLS

9168-9089，5395-5311

SA-iPLS

10000-9942，9875-9815，

9621-9561，9494-9434，

9430-9371，9112-9053，

7845-7782，7170-7106，

6495-6431，6427-6364、

6360-6296，6292-6229，

6225-6161，6157-6094，

6090-6026，6022-5959，

5955-5891，5887-5824，

5820-5756，5617-5553，

5347-5283，5212-5148，

5144-5081，5077-5013，

5009-4946，4874-4811，

4199-4135

Table 3. Modeling results of different models
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Table 3. Modeling results of different models
Model PLS factors $R_{M S E}^{V}$ $R_{M S E}^{T}$ R_T
PLS 19 0.0272 0.0140 0.8506
iPLS 2 0.0269 0.0155 0.7786
SiPLS 17 0.0260 0.0145 0.8420
SA-iPLS 17 0.0264 0.0117 0.8884

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Chunting Li, Youyou Zhang, Huazhou Chen, Jie Gu, Lina Mo, Xiaoyu Li. Near-Infrared Spectral Waveband Selection for Soil Potassium Content Based on Simulated Annealing[J]. Laser & Optoelectronics Progress, 2022, 59(13): 1330002

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