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基于近紅外傅里葉特征提取方法的土壤含水率檢測(cè)
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-infrared Spectral Detection of Soil Moisture Based on Feature Extraction of FFT
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    摘要:

    以湖北地區(qū)的3種土壤為研究對(duì)象,利用偏最小二乘法建立了處理后樣品的土壤含水率分析模型,模型預(yù)測(cè)值與標(biāo)準(zhǔn)值的決定系數(shù)為0.995,交叉驗(yàn)證預(yù)測(cè)均方差為0.801%,模型預(yù)測(cè)決定系數(shù)為0.992,預(yù)測(cè)均方差為0.912%,利用該模型預(yù)測(cè)黃土高原地區(qū)黃綿土含水率誤差均大于4%。利用近紅外光譜傅里葉變換特征提取方法對(duì)湖北地區(qū)黃棕壤、稻田土和潮土建立土壤含水率PLS預(yù)測(cè)模型,模型決定系數(shù)為0.988,交叉驗(yàn)證預(yù)測(cè)均方差為1.106%,且該模型預(yù)測(cè)黃綿土的誤差均在2%左右,精度較傳統(tǒng)模型有較大提高。

    Abstract:

    Three kinds of soil from Hubei province was studied, PLS and cross calibration method was employed to establish soil moisture analysis model. The result indicates that the model’s decision coefficient was 0.995,and RMSECV was 0.801%, the model’s forecast decision coefficient was 0.992, the RMSEP was 0.912%. Using the model to forecast loessial soil on Loess Plateau region, the error was greater than 4%. At last, the study of the Hubei yellow brown earth, paddy field soil and alluvial soil samples collected from Fourier transform parameters and characteristics of soil moisture to establish PLS prediction model, model decision coefficient was 0.988, and RMSECV is 1.106%. And the error of model prediction of loessial soil was less than 2%, the accuracy improved more greatly than traditional model.

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李小昱,肖武,李培武,雷廷武,王為,馮耀澤.基于近紅外傅里葉特征提取方法的土壤含水率檢測(cè)[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2009,40(5):64-67.-infrared Spectral Detection of Soil Moisture Based on Feature Extraction of FFT[J]. Transactions of the Chinese Society for Agricultural Machinery,2009,40(5):64-67.

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