Laser & Optoelectronics Progress, Volume. 58, Issue 16, 1630005(2021)
Regression Prediction of Soil Available Nitrogen Near-Infrared Spectroscopy Based on Boosting Algorithm
Figures & Tables(12)
Fig. 4. Technical route analysis of near-infrared hyperspectral characteristics of available nitrogen in soil
Fig. 5. Contrast of spectra before and after preprocess. (a) Original spectra; (b) SG; (c) LG; (d) FD; (e) SNV; (f) SG+SNV; (g) SG+LG; (h) SG+FD
Fig. 6. R2 and RPD values of regression models with testing obtained by different pretreatment methods. (a) R2; (b) RPD
Fig. 7. R2, RMSE, and RPD values of the testing sets of different algorithms. (a) R2;(b) RMSE; (c) RPD
Fig. 8. Optimal combination of wavelength points selected by different algorithms
Fig. 9. Measured and predicted values of PSO-AdaBoost model based on SNV in prediction set
Table 1. Statistical parameters of soil available nitrogen content
View tableTable 1. Statistical parameters of soil available nitrogen content
Dataset Number of samples Max / (mg·kg-1) Min / (mg·kg-1) Median / (mg·kg-1) Mean / (mg·kg-1) Standard / (mg·kg-1) Total set 188 731.584 19.32 132.644 179.083 144.398 Training set 131 731.584 19.32 130.732 179.440 148.217 Testing set 57 687.148 67.62 137.816 178.255 135.211
Table 2. Influence of different pretreatment methods on regression model
View tableTable 2. Influence of different pretreatment methods on regression model
Preprocess method Regression model Training set Testing set Parameter R2 RPD R2 RPD Number of latent variables SG PLSR 0.92 3.62 0.894 3.08 16 LG PLSR 0.94 4.02 0.898 3.14 11 FD PLSR 0.95 4.39 0.718 1.88 6 SNV PLSR 0.96 4.87 0.857 2.64 11 SG+SNV PLSR 0.94 4.16 0.845 2.54 11 SG+LG PLSR 0.95 4.25 0.916 3.45 14 SG+FD PLSR 0.88 2.87 0.738 1.95 6 Preprocess method Regression model Training set Testing set Parameter R2 RPD R2 RPD Learning rate/number of estimators SG GBRT 0.99 28.27 0.610 1.60 0.4/400 LG GBRT 0.99 28.27 0.508 1.43 0.2/200 FD GBRT 0.99 28.27 0.668 1.73 0.4/200 Preprocess method Regression model Training set Testing set Parameter R2 RPD R2 RPD Learning rate/number of estimators SNV GBRT 0.99 34.96 0.915 3.43 0.2/100 SG+SNV GBRT 0.99 15.13 0.910 3.33 0.4/100 SG+LG GBRT 0.99 28.27 0.573 1.53 0.4/100 SG+FD GBRT 0.99 22.27 0.898 3.14 0.1/300 SG AdaBoost 0.97 5.43 0.644 1.68 0.4/100 LG AdaBoost 0.95 4.68 0.576 1.54 0.4/200 FD AdaBoost 0.99 23.74 0.573 1.53 0.1/200 SNV AdaBoost 0.99 12.14 0.921 3.43 0.2/100 SG+SNV AdaBoost 0.99 20.07 0.912 3.37 0.1/200 SG+LG AdaBoost 0.96 4.75 0.319 1.21 0.3/200 SG+FD AdaBoost 0.99 28.27 0.876 2.84 0.1/200 SG XGBoost 0.99 28.24 0.745 1.98 0.1/300 LG XGBoost 0.99 28.24 0.739 1.95 0.1/300 FD XGBoost 0.99 28.26 0.470 1.15 0.2/100 SNV XGBoost 0.99 28.21 0.912 3.37 0.4/100 SG+SNV XGBoost 0.99 25.26 0.908 3.31 0.2/100 SG+LG XGBoost 0.99 28.25 0.745 1.98 0.1/300 SG+FD XGBoost 0.99 28.26 0.835 2.46 0.4/100 SG LightGBM 0.79 2.21 0.81 0.1/400 LG LightGBM 0.75 1.99 0.69 0.4/400 FD LightGBM 0.99 19.60 0.521 1.44 0.4/200 SNV LightGBM 0.99 26.87 0.849 2.57 0.4/100 SG+SNV LightGBM 0.99 25.44 0.857 2.65 0.4/100 SG+LG LightGBM 0.79 2.19 0.68 0.3/400 SG+FD LightGBM 0.99 22.62 0.695 1.81 0.1/200
Table 3. Analysis results of quantitative models with different variables based on the spectral data processed by SNV and SG+SNV
View tableTable 3. Analysis results of quantitative models with different variables based on the spectral data processed by SNV and SG+SNV
Preprocess method Algorithm Wavelength range /nm Number of variables Training set Testing set Parameter R2 RPD R2 RPD Learning rate/number of estimators SNV AdaBoost 350--1655 1305 0.99 12.14 0.921 3.43 0.2/100 SNV GBRT 350--1655 1305 0.99 34.96 0.915 3.43 0.2/100 SNV XGBoost 350--1655 1305 0.99 28.21 0.912 3.37 0.4/100 SNV RF-GBRT 600--999 200 0.99 28.27 0.922 3.57 0.4/300 SNV PSO-GBRT 602--999 202 0.99 28.27 0.924 3.63 0.2/400 SNV GA-GBRT 600--999 184 0.99 28.27 0.932 3.83 0.4/300 SNV SA-GBRT 602--998 206 0.99 28.27 0.941 4.11 0.4/300 SNV GGA-GBRT 601--999 202 0.99 28.27 0.919 3.52 0.2/400 SNV RF-AdaBoost 600--999 200 0.99 21.24 0.939 4.06 0.2/100 SNV PSO-AdaBoost 602--999 202 0.99 18.17 0.944 4.24 0.1/100 SNV GA- AdaBoost 600--999 184 0.99 12.95 0.940 4.09 0.4/100 SNV SA-AdaBoost 602--998 206 0.99 24.03 0.937 3.96 0.2/100 SNV GGA-AdaBoost 601--999 202 0.99 24.21 0.943 4.20 0.4/100 SNV RF-XGBoost 600--999 200 0.99 28.17 0.929 3.76 0.2/100 SNV PSO-XGBoost 602--999 202 0.99 28.25 0.821 3.36 0.2/400 SNV GA-XGBoost 600--999 184 0.99 28.22 0.886 2.96 0.3/100 SNV SA-XGBoost 602--998 206 0.99 20.21 0.834 2.46 0.1/200 SNV GGA-XGBoost 601--999 202 0.99 27.96 0.871 2.78 0.1/200 SG+SNV AdaBoost 350--1655 1305 0.99 20.07 0.912 3.37 0.1/200 SG+SNV GBRT 350--1655 1305 0.99 15.13 0.910 3.33 0.4/100 SG+SNV XGBoost 350--1655 1305 0.99 25.26 0.908 3.31 0.2/100 SG+SNV RF-GBRT 603--999 202 0.99 28.27 0.919 3.53 0.2/200 SG+SNV PSO-GBRT 607--999 201 0.99 28.27 0.913 3.39 0.1/300 SG+SNV GA-GBRT 604--999 209 0.99 28.27 0.927 3.69 0.4/100 Preprocess method Algorithm Wavelength range /nm Number of variables Training set Testing set Parameter R2 RPD R2 RPD Learning rate/number of estimators SG+SNV SA-GBRT 607--998 206 0.99 28.27 0.926 3.68 0.4/200 SG+SNV GGA-GBRT 607--999 200 0.99 28.27 0.900 3.17 0.3/400 SG+SNV RF-AdaBoost 603--999 202 0.99 12.12 0.919 3.51 0.3/100 SG+SNV PSO-AdaBoost 607--999 201 0.99 15.41 0.915 3.43 0.4/100 SG+SNV GA-AdaBoost 604--999 209 0.99 24.16 0.922 3.59 0.2/100 SG+SNV SA-AdaBoost 607--998 206 0.99 20.24 0.929 3.76 0.1/100 SG+SNV GGA-AdaBoost 607--999 200 0.99 12.45 0.924 3.64 0.3/300 SG+SNV RF-XGBoost 600--999 200 0.99 28.18 0.898 3.14 0.4/200 SG+SNV PSO-XGBoost 602--999 202 0.99 27.45 0.888 2.99 0.4/100 SG+SNV GA-XGBoost 600--999 184 0.99 24.22 0.885 2.95 0.3/100 SG+SNV SA-XGBoost 602--998 206 0.99 27.31 0.883 2.92 0.4/100 SG+SNV GGA-XGBoost 601--999 202 0.99 24.84 0.882 2.91 0.3/100
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Yalu Han, Shaowen Li, Wenrui Zheng, Shengqun Shi, Xianzhi Zhu, Xiu Jin. Regression Prediction of Soil Available Nitrogen Near-Infrared Spectroscopy Based on Boosting Algorithm[J]. Laser & Optoelectronics Progress, 2021, 58(16): 1630005
Paper Information
Category: Spectroscopy
Received: Aug. 28, 2020
Accepted: Sep. 20, 2020
Published Online: Aug. 16, 2021
The Author Email: Shaowen Li (shwli@ahau.edu.cn)
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