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Monitoring Soil Nitrate Nitrogen Based on Hyperspectral Data in the Apple Orchards

机译:基于高光谱数据的苹果园土壤硝酸盐氮监测

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摘要

This paper is aimed to monitor the soil nitrate nitrogen content in the apple orchards rapidly, accurately and in real time by making full use of the effective information of soil spectra. The 96 air-dried soil samples of the apple orchards in Qixia county, Yantai city, Shandong province were used as the data source. Spectral measurements of soil samples were carried out by ASD Fieldspec 3 in the darkroom, and the content of the soil nitrate nitrogen was determined by chemical method. Then the hyperspectral reflectance of soil samples were preprocessed by Multivariate Scatter Correction (MSC) and First Derivative (FD), the correlation analysis was carried out with the soil nitrate nitrogen content. The sensitive wavelength of soil nitrate nitrogen was screened. Finally, the Support Vector Machine (SVM) model for the soil nitrate nitrogen content was established. The results showed that the selected sensitive wavelength were 617 nm, 760 nm, 1239 nm, 1442 nm, 1535 nm, 1695 nm, 1776 nm, 1907 nm and 2088 nm. Hyperspectral monitoring model was established by SVM, in which the prediction set R2 was 0.959, RMSE was 0.281, RPD was 3.835; the correction set R2 was 0.822, RMSE was 0.392, RPD was 2.037. The SVM model could be used to monitor the soil nitrate content accurately.
机译:本文旨在通过充分利用土壤光谱的有效信息,快速,准确,实时地监测苹果园土壤硝酸盐氮含量。以山东省烟台市栖霞县苹果园的96个风干土壤样品为数据源。在暗室中通过ASD Fieldspec 3对土壤样品进行光谱测量,并通过化学方法测定土壤硝酸盐氮的含量。然后通过多元散射校正(MSC)和一阶导数(FD)对土壤样品的高光谱反射率进行预处理,并对土壤硝酸盐氮含量进行相关分析。筛选了土壤硝态氮的敏感波长。最后,建立了土壤硝酸盐氮含量的支持向量机模型。结果表明,选择的敏感波长为617nm,760nm,1239nm,1442nm,1535nm,1695nm,1776nm,1907nm和2088nm。利用支持向量机建立高光谱监测模型,预测集R2为0.959,RMSE为0.281,RPD为3.835;校正集R2为0.822,RMSE为0.392,RPD为2.037。 SVM模型可用于准确监测土壤硝酸盐含量。

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