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Aiming at the shortcomings of traditional methods for detecting the content of Alkaline Hydrolysis Nitrogen (AHN) and pH value in soil, such as time-consuming and labor-consuming, this paper proposes a rapid quantitative inversion method based on hyperspectral analysis of AHN content and pH value. This method uses db4 discrete wavelet denoising (DWD) and wavelet denoising normalization (DWD-N) to carry out Pearson correlation analysis, and two methods, Ridge regression and Partial Least Squares Regression (PLSR), were used to compare the accuracy of hyperspectral inversion of soil AHN content and pH value. Experiments have demonstrated that in the inversion of the AHN content prediction model, Ridge regression has a good modeling effect under the DWD-N model, where R 2 =0.647, RMSE=7.067mg/kg. PLSR has good prediction effect under DWD-N, where R 2  is the highest of 0.792, RMSE is 3.438mg/kg; in the model inversion of pH prediction, the full-band PLSR modeling effect of pH value under DWD pretreatment is the best, which modeling set and the prediction set of R 2  is 0.826 and 0.875, the RMSE is 0.217 mg/kg and 0.191 mg/kg respectively.

Research on Hyperspectral Inversion of Soil Alkaline Hydrolysis Nitrogen Content and pH Value Based on DWD