Research On The Predicting Methods Of Soil Available Nitrogen With Hyperspectral Data

Soil available nitrogen is one of the important nutrients in the growth and development of crops.Its content is related to the organic matter content of the soil.It can reflect the recent supply of soil nitrogen and provide fast and accurate information on soil available nitrogen.It is of great significance to guide the precise fertilization and promote the development of modern agriculture.The traditional soil nutrient information deteetion uses chemical detection methods,which have high requirements for testing personnel,and have problems such as low detection efficiency,high cost,and easy environmental pollution,which cannot meet the development requirements of modern precision agriculture.In recent years,because the visible near-infrared hyperspectral analysis technology has the advantages of easy operation and no pollution,it has been paid more and more attention in the quantitative determination of soil nutrients.In this study,the high-spectrum technique for detecting soil available nitrogen content is not accurate,and the method of detecting available nitrogen in yellow-red loam soil in southern Anhui Province is the research object,which focuses on solving two key problems in soil rapid-acting nitrogen hyperspectral detection model——optimization of pretreatment methods for soil available nitrogen detection and optimization of soil available nitrogen hyperspectral detection model.The effects of different pretreatment methods on the visible near-infrared(vis-NIR)hyperspectral prediction model of soil available nitrogen were analyzed and compared.The hyperspectral multi-model fusion regression prediction method for soil available nitrogen was studied.The main research contents and results are as follows:(1)The effects of different pretreatment methods on soil available nitrogen vis-NIR hyperspectral prediction model were compared.A non-imaging hyperspectral acquisition system was constructed,and the average spectral reflectance curve of the 350-1657nm vis-NIR in yellow-red loam was analyzed.Correction modeling using the original spectrum and the different pre-transformed spectra(30 spectra in total)combined with linear partial least squares regression(PLSR)algorithm and nonlinear partial least squares regression algorithm(RBF-PLSR).The research results show that different preprocessing methods have a great influence on the modeling effect.After the SG filtering smoothing and its combination algorithm are processed,the prediction performance of the model can be improved,but the derivative spectrum and its combination are preprocessed and transformed.The prediction performance of the model is reduced,and the model with the multi-scatter correction and the combined pre-processed transformed spectrum has the worst prediction performance.Comparing the prediction effects of linear and nonlinear PLSR models,the prediction effect of linear PLSR is better than nonlinear PLSR as a whole.Finally,the SGG+LG/PLSR correction model was selected as the optimal combination correction model for soil available nitrogeiL The modeling set R2=0.94,RPD=3.88,prediction set R2=0.91,RPD=3.38,the prediction accuracy of the model reached A.The class has a very good predictive effect,which lays a foundation for selecting appropriate soil spectral data preprocessing correction method and establishing an effective soil available nitrogen prediction model.(2)A hyperspectral multi-model fusion regression prediction method for soil available nitrogen was studied.Firstly,the characteristics of the original spectral reflectance curve of 350-1657nm vis-NIR in yellow red loam were analyzed.Then using the visible near-infrared hyperspectral(350-1607nm)data of the soil,based on the linear and nonlinear kernel functions,nine different prediction models were constructed by the parameter tuning method of the network format search.Finally,based on the integrated learning algorithm,multiple models are merged,and the random forest algorithm is used as the secondary model for predictive modeling.The results show that the fusion results do not increase with the increase of the number of models.In this study,four models are extracted from nine models,which are partial least squares regression(PLSR)and Sigmoid function.Least Squares Regression(Sigrnoid-PLSR),Support Vector Regression(SVR),and Sigmoid-Ridge of Sigmoid Function.After fusion,the prediction accuracy of soil available nitrogen is optimal.The model set of soil available nitrogen after multi-model fusion was R2=0.96,RPD=4.89,and the prediction set R2=0.94,RPD=4.16,which showed a signifieant improvement in accuracy and stability compared with the single model.Therefore,the method based on multi-model fusion can improve the accuracy and accuracy of the soil available nitrogen prediction model and optimize the hyperspectral prediction ability of soil available nitrogen.