Construction and Evaluation of Prediction Model of Main Soil Nutrients Based on Spectral Information

The rapid and accurate detection of soil nutrient content through spectral technology is one of the requisite technologies for precision fertilization, which, however, is an unsolved issue. In order to achieve this purpose, a more robust and accurate model is established in this study. The regression algorithm is integrated with effective wavelength selection to construct the prediction model for total nitrogen, available phosphorus, and available potassium (N, P, and K), which removes the need for complex pretreatment and algorithm constraints. According to the research results, with regard to the prediction of soil nitrogen, phosphorus, and potassium contents, the joint interval support vector regression (Si-SVR) model performed best in modeling, with the root mean square error of prediction (RMSEP) limited to 0.0231%, 1.0554%, and 3.4225%, respectively. In addition, the relative percentage deviation (RPD) values were restricted to 2.68, 2.12, and 2.37, respectively. As indicated by the prediction results obtained for the above three nutrient contents, the RPD values of the Si-SVR model prediction accuracy evaluation indicators exceeded 2.0, which evidences a high level of prediction accuracy. This method makes it possible for spectral data to be applied in practical production, and these results provide a valuable reference for the effective detection of major soil nutrients.