基于近红外光谱和支持向量机的土壤参数预测  

Forecasting soil parameters based on NIR and SVM

作　　者：郑立华[1] 李民赞[1] 安晓飞[1] 孙红[1] 

机构地区：[1]中国农业大学现代精细农业系统集成研究教育部重点实验室,北京100083

出　　处：《农业工程学报》2010年第S2期81-87,共7页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家863计划(2006AA10A301);教育部青年教师基本科研业务经费项目(2009JS105)

摘　　要：应用支持向量机算法对实时土壤光谱数据进行处理,获得了土壤全氮和有机质的回归模型并研究了模型随参数变化的规律。从中国农业大学试验田采集了150个土样,用光谱仪获取了原始土壤样本的近红外光谱,用实验室分析法获取了各样本的全氮和有机质含量。以近红外光谱数据为自变量对2个土壤参数进行了回归建模并评价了算法各参数对模型的影响。研究表明土壤参数适合于全谱支持向量回归。对于土壤全氮,基于小波降噪NIR光谱的SVM回归模型的标定R2为0.9224,验证R2为0.3667;对于土壤有机质,基于原始NIR光谱的SVM回归模型的标定R2为0.9179,验证R2为0.4152;对经k-means聚类分析后的50个样本进行回归建模结果表明,标定R2和验证R2均有提高。By using SVM(Support Vector Machine) algorithm,the spectra of real-time soil samples were processed.Therefore the soil TN(Soil Total Nitrogen) and SOM(Soil Organic Matter) regression models were acquired and analysis on model accuracy which changed with the parameters of SVM was conducted.First,150 soil samples were collected from the experimental field of China Agricultural University.Then the spectrum of each original soil sample was detected with the NIR(Near Infrared Reflectance) spectrograph.And the contents of TN and SOM in each sample were measured by using laboratory analysis methods.Finally,the regression models for TN and SOM were established based on SVM,and the evaluation about the extent was studied,which parameters of SVM could produce an effect on the predicting models.Research showed that it was befitting for the TN and SOM to established prediction models based on the whole spectra by using SVM.For soil TN,the calibration R2 reached to 0.9224 and validation R2 was 0.3667 based on the denoised spectra.For SOM,the calibration R2 reached to 0.9179 and validation R2 was 0.4152 based on the original NIR spectra.And the predicting accuracies for both TN model and SOM model were acceptable with low root-mean-square deviation of regression and validation.

关 键 词：支持向量机 光谱 土壤 氮 有机质 预测 

分 类 号：S123[农业科学—农业基础科学] S15-3