## 4 Conclusions  
With the decrease of soil water content, soil spectral reflectance increased nonlinearly. The soil spectrum is largely affected by soil water content, which masks the spectral absorption information of the main soil nutrients (N, P, and K). In this study, the samples were not treated with special water content, and the uniform soil particles naturally dried and sieved were taken as the research object. In addition, integrated PLS, Si-PLS, SVR, and Si-SVR models were adopted to predict the contents of N, P, and K in soil samples. In the stage of spectral preprocessing, the moving average method was used to denoise the measured spectrum. In this paper, PLS and SVR, as the two most widely used methods of quantitative analysis, were applied to establish the prediction model of soil main nutrient elements (N, P, and K) based on spectral information under laboratory conditions. However, the spectral data carried a lot of information about the samples to be measured, and there was some redundant information in these data. In order to reduce the impact of such information on prediction accuracy while improving the robustness and accuracy of the prediction model, effective wavelength selection was carried out for the full spectrum band. The results show that under the same conditions of data preprocessing, the prediction accuracy of the Si-SVR model was the highest for N and K, with the _RPD_ values reaching 2.68 and 2.37, respectively. The _RPD_ value of the prediction model for P is relatively lower compared to the SVR model, but the _RPD_ of the Si-SVR exceeds 2. In addition, the prediction effect of the model is better. Upon comprehensive comparison, the Si-SVR model is considered better in terms of generalization. The ultimate purpose of this study is to provide a theoretical reference for the construction of cotton precision fertilization decision-making systems. In practice, there are demanding requirements placed on the timeliness of testing instruments, which means the testing speed must be higher. In this study, an effective model was constructed to predict the main soil nutrients. To sum up, attention should be paid to the timeliness when spectral data are processed, so as to minimize the redundant information of the input model, which is conducive to improving the detection efficiency. Therefore, characteristic band screening and regression model were combined in this study to screen the input original spectra and extract the variables related to soil nutrients as the input information of the model, which significantly reduces the redundant information between variables. On this basis, a high-accuracy prediction model of main soil nutrients was established, which lays a foundation for the research on feasibility of rapid detection of soil nutrients, thus meeting the requirements of precision agriculture. **Author Contributions:** This study was conducted jointly by all team members. The work undertaken by each member is as follows: Conceptualization, S.Y. ; methodology, S.Y. and H.B. ; software, S.Y. ; validation, Z.J., W.D. and H.B. ; formal analysis, S.Y. ; investigation, Z.J. ; writing--original draft preparation, S.Y. ; visualization, Y.X. and L.Z. ; supervision, L.Z. ; project administration, W.D. ; funding acquisition, Y.X. All authors have read and agreed to the published version of the manuscript. **Funding:** This work is funded by the Science and Technology Bureau of Xinjiang production and Construction Corps, the key field science and technology research project No. 2020 AB002 (\"research and development of cotton sowing, fertilization and mulching combined machine based on Beidou Positioning intelligent control\"). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.