Nitrogen, phosphorus, and potassium prediction in soils, using infrared spectroscopy

The aim of this study was to investigate the potential of the infrared spectroscopy technique for non-destructive measurement of soil properties. For the study, 280 soil samples were collected from several regions in Zhejiang, China. Data from near infrared (NIR, 800–2500 nm), mid infrared (MIR, 4000–400 cm–1), and the combined NIR–MIR regions were compared to determine which produced the best prediction of soil properties. Least-squares support vector machines (LS-SVM) were applied to construct calibration models for soil properties such as available nitrogen (N), phosphorus (P), and potassium (K). The results showed that both spectral regions contained substantial information on N, P, and K in the soils studied, and the combined NIR–MIR region did a little worse than either the NIR or MIR region. Optimal results were obtained through LS-SVM compared with the standard partial least-squares regression method, and the correlation coefficient of prediction (rp), root mean square error for prediction, and bias were, respectively, 0.90, 16.28 mg/kg, and 0.96 mg/kg for the prediction results of N in the NIR region; and 0.88, 41.62 mg/kg, and –2.28 mg/kg for the prediction results of P, and 0.89, 33.47 mg/kg, and 2.96 mg/kg for the prediction results of K, both in the MIR region. This work demonstrated the potential of LS-SVM coupled to infrared reflectance spectroscopy for more efficient soil analysis and the acquisition of soil information.