Extracting Characteristic Wavelength of Soil Nutrients Based on Multi-Classifier Fusion
LI Xue-ying1, 2, 3, FAN Ping-ping1, 2, 3, LIU Yan1, 2, 3*, WANG Qian1, 2, 3, Lü Mei-rong1, 2, 3*
1. Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266061, China
2. Shandong Provincial Key Laboratory of Ocean Environmental Monitoring Technology, Qingdao 266061, China
3. National Engineering and Technological Research Center of Marine Monitoring Equipment, Qingdao 266061, China
Abstract：Although spectral technology has been applied to the rapid detection of soil nutrient, how to find the spectral characteristic bands of soil, to avoid useless information and to keep useful information, and to establish a model with high accuracy and good predictive effect is still an urgent problem to be solved. Taking soil samples from three different regions in Qingdao as an example, the ultraviolet-visible-near-infrared spectra and total carbon (TC), total nitrogen (TN) and total phosphorus (TP) content of soil samples were determined. Successive Projections Algorithm (SPA), Uninformative Variable Elimination (UVE), Genetic Algorithm (GA) and Correlation Coefficient Method (CC) four kinds of algorithms (four single classifiers) were used to extract the characteristic wavelength of the soil spectra. The multi-classifier fusion of the voting method and the weighted voting method were used to obtain the characteristic wavelength. The soil nutrient content models were established by the partial least squares regression (PLSR). Through theresult of these models (the determination coefficient of calibration set R2c, the corrected root mean square error RMSEC, the determination coefficient of test set R2p, the predicted root mean square error RMSEP and residual predictive deviation RPD), we evaluated the effect of extracting the characteristic wavelength of soil nutrient content among each single classifier algorithm and multiple-classifier fusion algorithm. In this paper, the multi-classifier fusion of four algorithms, three algorithms and optimal two algorithms were analyzed. The results showed that, after merging four kinds of single classifier by voting method, the model effect was mostly inferior to each single classifier, and there were many characteristic wavelengths in the relative good model. The model effect of four single classifier by weighted voting method had been improved compared with that by voting method. TC and TN could achieve better prediction effect in less wavelength, but only after TN fusion, the model effect was better than each single classifier. TC, TN and TP were fused by weighted voting method with SPA+UVE+GA, SPA+UVE+GA (or SPA+GA+CC) and SPA+UVE+GA three kinds of single classifier, and the optimal model effect was obtained, which was superior to each single classifier. The soil nutrient content was fused by weighted voting method with two optimal single classifier, the modeling effect was better than that of the optimal single classifier, the results of TC and TP modeling were slightly worse than those of three single classifiers, and TN modeling effect was the same as that of three single classifiers. So TC, TN and TP could obtain higher results than single classifier in case of selecting three kinds of algorithms and including the optimal two algorithms. It provides a new method for finding spectral characteristic bands of soil nutrients and other complex substances, and also provides a new idea for the comprehensive application of various algorithms.
李雪莹，范萍萍，刘 岩，王 茜，吕美蓉. 多分类器融合提取土壤养分特征波长[J]. 光谱学与光谱分析, 2019, 39(09): 2862-2867.
LI Xue-ying, FAN Ping-ping, LIU Yan, WANG Qian, Lü Mei-rong. Extracting Characteristic Wavelength of Soil Nutrients Based on Multi-Classifier Fusion. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(09): 2862-2867.
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