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Near-Infrared Spectroscopy Technology for Soil Nutrients Detection Based on LS-SVM

机译:基于LS-SVM的土壤养分检测近红外光谱技术。

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The detection method of the soil nutrients (organic matter and available N, P, K) were analyzed based on the near infrared spectroscopy technology in order to decision-making for precision fertilization. 54 samples with 7mx7m was collected using DGPS receiver positioning in a soybean field. The soil organic matter, available nitrogen (N), available phosphorus (P), available potassium (K) content was determined, the near-infrared diffuse reflectance spectrum of the soil samples were obtained by FieldSpec3 spectrometer. 54 samples were randomly divided into 40 prediction sets and 14 validation sets. After smoothing, the eight principal components of original spectra were extracted by principal component analysis (PCA). Prediction model of soil organic matter, available nitrogen (N), available phosphorus (P), potassium (K) were respectively established with the eight principal component as input and soil nutrients by measured as the output, and the 14 validation samples were predicted. The results showed that the soil organic matter, available nitrogen (N), available phosphorus (P), potassium (K) prediction model were set up with principal component analysis and LS-SVM, which the correlation coefficients between the prediction value and measurement value were 0.8708, 0.7206, 0.8421 and 0.6858, the relative errors of the LS-SVM prediction was smaller and those mean values were 1.09%, 1.06%, 4.08% and 0.69%. The method of soil organic matter content prediction is feasible.
机译:基于近红外光谱技术,分析了土壤养分(有机质和有效氮,磷,钾)的检测方法,为精准施肥提供决策依据。使用定位在大豆田中的DGPS接收器收集了54个7mx7m的样本。测定了土壤有机质,有效氮(N),有效磷(P),有效钾(K)含量,并通过FieldSpec3光谱仪获得了土壤样品的近红外漫反射光谱。将54个样本随机分为40个预测集和14个验证集。平滑后,通过主成分分析(PCA)提取原始光谱的八个主成分。以8个主成分为输入,以土壤养分为输出,分别建立了土壤有机质,有效氮(N),有效磷(P),钾(K)的预测模型,并预测了14个验证样本。结果表明,利用主成分分析和LS-SVM建立了土壤有机质,有效氮(N),有效磷(P),钾(K)的预测模型,其预测值与测量值之间具有相关系数。分别为0.8708、0.7206、0.8421和0.6858,LS-SVM预测的相对误差较小,平均值分别为1.09%,1.06%,4.08%和0.69%。土壤有机质含量的预测方法是可行的。

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