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基于PCA_SVR的油菜氮素光譜特征定量分析模型
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Content Spectral Character Models Based on PCA_SVR Method
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    摘要:

    研究了采用光譜分析技術(shù)對(duì)油菜植株全氮進(jìn)行定量分析的方法,。采用逐步回歸法對(duì)氮素的光譜特征波長(zhǎng)進(jìn)行選擇,,為克服光譜變量間多重共線性的影響,,對(duì)變量進(jìn)行了主成分分析(PCA),,為提高模型的擬合優(yōu)度,,應(yīng)用支持向量機(jī)回歸(SVR)建立油菜氮素的定量分析模型,。對(duì)不同氮素水平的油菜冠層光譜數(shù)據(jù)進(jìn)行分析,,結(jié)果表明,,406,、460,、556、634,、662,、675nm的光譜反射率與油菜含氮量呈極顯著相關(guān)。植株全氮SVR模型預(yù)測(cè)值與實(shí)測(cè)值的相關(guān)系數(shù)為0.89,,模型的檢驗(yàn)誤差(

    Abstract:

    RMSE)為2.51,。It was studied that using spectral analysis to quantitatively analyze the rape total nitrogen content. Stepwise regression was used to select the characteristic wavelength of total nitrogen content against rape leaf spectra for nitrogen content prediction. The method of principal component analysis (PCA) was used to avoid the effect of multiple co-linearity among the spectral data. In order to enhance model forecast precision, the method of support vector machine regression(SVR)was used to establish the model between the rape total nitrogen content and the spectral characteristic wavelength data. From the rape spectral data under different nitrogen level, it was found that the linear relationships between rape total nitrogen content and spectral reflectance value of 406, 460, 556, 634, 662, 675nm are very notable. The correlation coefficient between the predict value and the real value is 0.89, the RMSE is 2.51.

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張曉東,毛罕平,程秀花.基于PCA_SVR的油菜氮素光譜特征定量分析模型[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2009,40(4):161-165. Content Spectral Character Models Based on PCA_SVR Method[J]. Transactions of the Chinese Society for Agricultural Machinery,2009,40(4):161-165.

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