Abstract

Applying Near Infrared Reflectance Spectroscopy (NIRS) on farmlands can effectively estimate the available phosphorus and potassium contents of soil online. Spectral preprocessing, including Savitzky Golay (SG), Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC) and SG 1st derivative, aimed to eliminate system noise and external interference. A correction model was created using respectively Radial Basis Function (RBF) and Least Squares Support Vector Machine (LS-SVM) methods with input from the characteristic wavelengths obtained using Successive Projections Algorithm (SPA). The results of predicting available phosphorus and potassium contents in soil using these two modeling methods were evaluated and the better model was selected. The results showed that the LS-SVM method with input from the characteristic wavelengths obtained using SPA had an advantage over the RBF modeling method. In SPA-LS-SVM models, the correlation coefficient and mean square error of prediction for available phosphorus were 0.8625 and 8.67 and those for available potassium were 0.7843 and 13.42, respectively. This indicates that SPA-based visible-near-infrared spectroscopy using LS-SVM for modeling can be used as a method to accurately measure available phosphorus and potassium contents in soil.

Keywords:

LS-SVM, RBF, Spectroscopy, SPA,

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