基于近红外光谱技术的土壤参数BP神经网络预测

Estimation of Soil Organic Matter and Soil Total Nitrogen Based on NIR Spectroscopy and BP Neural Network

利用BP神经网络预测方法,建立了基于近红外光谱技术的土壤有机质含量和土壤全氮含量的分析模型。试验共测量了150个田间土壤样本的近红外光谱,首先采用局部加权散点图平滑滤波法对光谱曲线进行了平滑处理,然后根据对目标参数进行的聚类分析结果进一步平均了输入光谱,最后将反射光谱数据进行对数转换后与目标数据一起进行了归一化处理。对预处理后的光谱数据首先进行主成分分析,然后提取贡献率超过99.98%的主成分建立BP神经网络模型。对土壤有机质含量的分析结果:模型拟合精度为0.999,预测精度达到0.854。对于土壤全氮含量的分析结果:模型的拟合精度近似为1,预测精度达到了0.808。研究表明,基于近红外光谱技术的土壤参数BP神经网络预测模型具有较高的鲁棒性和较强的容错能力。

Estimation models of soil organic matter （SOM） and soil total nitrogen （TN） were established based on NIR spectroscopy and BP neural network. A total of 150 soil samples were collected from the tested farm, and the NIR spectra of all soil sampies were measured. First, data pretreatment was performed for each sample with the method of locally weighted scatter plot smooth filtering. Then the box plot analysis for the measured SOM data and TN data were conducted separately and the information about the shape, location, and distribution of the target data was obtained. The variance between the SOM data was very small, and most of them were concentrated on the median. This was also observed from TN data. Thus clustering analysis was carried out for the target parameters of the soil samples so that the original dataset with 150 spectra was clustered to 50 groups. For each group, the average of spectral data was calculated at every wavelength to obtain a new spectrum. The new spectrum was calculated with natural logarithm and normalized, which was taken as a new sample. Principal component analysis （PCA） was executed for 50 new samples and the principal components with over 99. 98% of cumulative proportion of correlation matrix were extracted to establish BP neural network. According to the analysis result of SOM content, the calibration accuracy of the model was 0. 999, and the validation accuracy reached to 0. 854. According to the analysis result of the soil TN content, the calibration accuracy of the model was close to 1, and the validation accuracy reached 0. 808. The result shows that the smooth filter can weaken the noise in the data, expose the data features, provide a reasonable starting approach for parametric fitting; and improve the prediction accuracy; It is feasible and practical to estimate soil parameters by using BP neural network with the predic-tion accuracy of 0. 854 （SOM） and 0. 808 （TN）; Compared to. the other prediction modeling method, the BP neural network model has higher robustness and better fault tolerance, and the model accuracy would not be affected by the several outline samples when the number of samples is large enough.