基于近红外光谱波长优选的土壤有机质含量预测研究
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摘要
近红外光谱技术是检测土壤信息的有效工具,为了提高预测模型的准确度和建模效率,需要对波长进行优选。提出SiPLS-GA-SPA特征波长提取方法,即协同区间偏最小二乘算法(SiPLS)、遗传算法(GA)和连续投影算法(SPA)对土壤有机质特征波长进行梯度提取,最终从1 050个波长中提取9个土壤有机质的特征波长。利用偏最小二乘回归(PLSR)和支持向量机回归(SVMR)建立6种基于特征波长的土壤有机质含量预测模型。结果表明:SiPLS-GA-SPA-SVMR模型的预测结果为RMSEP=1.15,R2=0.91,优于其他模型;SiPLS-GA-SPA特征波长提取方法能够简化预测模型,提高模型预测精度,为开发便携式近红外光谱土壤养分检测仪提供理论基础。
The near infrared spectroscopy technology is an effective tool for detecting soil information,and wavelength optimization is necessary to improve the accuracy and modeling efficiency of the prediction model. Therefore,an SiPLS-GA-SPA feature wavelength extraction method is proposed. The synergy interval partial least squares(SiPLS),genetic algorithm(GA)and successive projection algorithm(SPA) are combined to conduct gradient extraction for feature wavelengths of soil′ s organic matter,and 9 feature wavelengths of soil′s organic matter are extracted from 1050 wavelengths. The partial least squares regression(PLSR)and support vector machine regression(SVMR)are adopted to establish 6 soil′ s organic matter content prediction models based on feature wavelengths. The results show that,the prediction results of the SiPLS-GA-SPA-SVMR model(RMSEP=1.15,R2=0.91)are superior to other models,and the SiPLS-GA-SPA feature wavelength extraction method can simplify the prediction model and improve the prediction accuracy of the model,which provides a theoretical basis for the development of the portable near infrared spectroscopy soil nutrient detector.
The near infrared spectroscopy technology is an effective tool for detecting soil information,and wavelength optimization is necessary to improve the accuracy and modeling efficiency of the prediction model. Therefore,an SiPLS-GA-SPA feature wavelength extraction method is proposed. The synergy interval partial least squares(SiPLS),genetic algorithm(GA)and successive projection algorithm(SPA) are combined to conduct gradient extraction for feature wavelengths of soil′ s organic matter,and 9 feature wavelengths of soil′s organic matter are extracted from 1050 wavelengths. The partial least squares regression(PLSR)and support vector machine regression(SVMR)are adopted to establish 6 soil′ s organic matter content prediction models based on feature wavelengths. The results show that,the prediction results of the SiPLS-GA-SPA-SVMR model(RMSEP=1.15,R2=0.91)are superior to other models,and the SiPLS-GA-SPA feature wavelength extraction method can simplify the prediction model and improve the prediction accuracy of the model,which provides a theoretical basis for the development of the portable near infrared spectroscopy soil nutrient detector.
引文
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[10]宾俊,范伟,周冀衡,等.智能优化算法应用于近红外光谱波长选择的比较研究[J].光谱学与光谱分析,2017,37(1):95-102.BIN Jun,FAN Wei,ZHOU Jiheng,et al. Application of intelligent optimization algorithms to wavelength selection of near-infrared spectroscopy[J]. Spectroscopy and spectral analysis,2017,37(1):95-102.
[11]章海亮,罗微,刘雪梅,等.应用遗传算法结合连续投影算法近红外光谱检测土壤有机质研究[J].光谱学与光谱分析,2017,37(2):584-587.ZHANG Hailiang,LUO Wei,LIU Xuemei,et al. Measurement of soil organic matter with near infrared spectroscopy combined with genetic algorithm and successive projection algorithm[J]. Spectroscopy and spectral analysis, 2017, 37(2):584-587.
[12] MORELLOS A,PANTAZI X E,MOSHOU D,et al. Machine learning based prediction of soil total nitrogen,organic carbon and moisture content by using VIS-NIR spectroscopy[J].Biosystems engineering,2016,152:104-116.
[13] ROSSEL R A V,BEHRENS T,GUERRERO C,et al. Using data mining to model and interpret soil diffuse reflectance spectra[J]. Geoderma,2010,158(1):46-54.
[2]刘燕德,熊松盛,刘德力.近红外光谱技术在土壤成分检测中的研究进展[J].光谱学与光谱分析,2014,34(10):2639-2644.LIU Yande,XIONG Songsheng,LIU Deli. Application of near infrared reflectance spectroscopy technique(NIRS)to soil attributes research[J]. Spectroscopy and spectral analysis,2014,34(10):2639-2644.
[3] RINNAN R,RINNAN A. Application of near infrared reflectance(NIR)and fluorescence spectroscopy to analysis of microbiological and chemical properties of arctic soil[J]. Soil biology&biochemistry,2007,39(7):1664-1673.
[4]杨峰,张勇,谌俊旭,等.高光谱数据预处理对大豆叶绿素密度反演的作用[J].遥感信息,2017,32(4):64-69.YANG Feng,ZHANG Yong,CHEN Junxu,et al. Effects of hyperspectral data pretreatment on model inversion of soybean chlorophyll density[J]. Remote sensing information,2017,32(4):64-69.
[5]陈奕云,齐天赐,黄颖菁,等.土壤有机质含量可见-近红外光谱反演模型校正集优选方法[J].农业工程学报,2017,33(6):107-114.CHEN Yiyun,QI Tianci,HUANG Yingjing,et al. Optimization method of calibration dataset for VIS-NIR spectral inver-sion model of soil organic matter content[J]. Transactions ofthe Chinese Society of Agricultural Engineering, 2017, 33(6):107-114.
[6] GALV?O R K H,ARAUJO M C U,JOSéG E,et al. A meth-od for calibration and validation subset partitioning[J]. Talan-ta,2005,67(4):736-740.
[7]王瑛瑛.土壤有机质近红外光谱分析及相关软件开发[D].合肥:中国科学技术大学,2014.WANG Yingying. The near-infrared spectroscopy analysis of organic matter and related software development[D]. Hefei:University of Science and Technology of China,2014.
[8] YANG M,CHEN Q,KUTSANEDZIE F Y H,et al. Portable spectroscopy system determination of acid value in peanut oil based on variables selection algorithms[J]. Measurement,2017,103:179-185.
[9]杨海清,祝旻.基于可见-近红外光谱特征波长选择的土壤有机质快速检测研究[J].红外,2015,36(2):42-48.YANG Haiqing,ZHU Min. Study of rapid detection of soil organic matter based on characteristic wavelength selection of visible-near infrared spectra[J]. Infrared,2015,36(2):42-48.
[10]宾俊,范伟,周冀衡,等.智能优化算法应用于近红外光谱波长选择的比较研究[J].光谱学与光谱分析,2017,37(1):95-102.BIN Jun,FAN Wei,ZHOU Jiheng,et al. Application of intelligent optimization algorithms to wavelength selection of near-infrared spectroscopy[J]. Spectroscopy and spectral analysis,2017,37(1):95-102.
[11]章海亮,罗微,刘雪梅,等.应用遗传算法结合连续投影算法近红外光谱检测土壤有机质研究[J].光谱学与光谱分析,2017,37(2):584-587.ZHANG Hailiang,LUO Wei,LIU Xuemei,et al. Measurement of soil organic matter with near infrared spectroscopy combined with genetic algorithm and successive projection algorithm[J]. Spectroscopy and spectral analysis, 2017, 37(2):584-587.
[12] MORELLOS A,PANTAZI X E,MOSHOU D,et al. Machine learning based prediction of soil total nitrogen,organic carbon and moisture content by using VIS-NIR spectroscopy[J].Biosystems engineering,2016,152:104-116.
[13] ROSSEL R A V,BEHRENS T,GUERRERO C,et al. Using data mining to model and interpret soil diffuse reflectance spectra[J]. Geoderma,2010,158(1):46-54.