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Position: Home > Articles > Soil Total Nitrogen Content Prediction Based on Gray Correlation-extreme Learning Machine Transactions of the Chinese Society for Agricultural Machinery 2017 (1) 271-276

基于灰度关联-极限学习机的土壤全氮预测

作  者：
周鹏;杨玮;李民赞;郑立华;陈玉青
单  位：
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
关键词：
近红外光谱;波长选择;灰度关联;土壤全氮;极限学习机
摘  要：
为了克服近红外光谱的多重共线性、吸光度非线性等特点给土壤全氮含量预测带来的影响,引入灰度关联-极限学习机方法选择出具有较好预测能力的波长组合,以建立高精度土壤全氮含量预测模型。首先利用一阶微分光谱得到反映土壤全氮含量的敏感谱区,再利用灰度关联法得到土壤全氮含量的敏感波长,分别为1 007、1 128、1 360、1 596、1 696、1 836、2 149、2 262 nm。最后采用极限学习机,将上述敏感波长作为输入,建立了土壤全氮预测模型。作为对照,同时采用传统相关分析方法选择了敏感波长并建立了回归模型。2种建模结果表明,灰度关联-极限学习机建立的土壤全氮预测模型,其建模决定系数R_c~2为0.913 4,预测决定系数R_v~2为0.878 7,建模精度和预测精度都比传统建模方法高。特别在预测低氮含量土壤时,灰度关联-极限学习机方法优势更明显。
译  名：
Soil Total Nitrogen Content Prediction Based on Gray Correlation-extreme Learning Machine
作  者：
ZHOU Peng;YANG Wei;LI Minzan;ZHENG Lihua;CHEN Yuqing;Key Laboratory on Modern Precision Agriculture System Integration Research,Ministry of Education,China Agricultural University;
关键词：
near infrared spectroscopy;;wavelength selection;;gray correlation;;soil total nitrogen;;extreme learning machine
摘  要：
In order to overcome the influences of multi-collinearity and absorbance non-linearity in nearinfrared spectroscopy on predicting soil total nitrogen content,the gray correlation-extreme learning machine method was used to select the combination wavebands with good prediction capability to establish high precision prediction model for soil total nitrogen content.First,the first derivative spectra was used to get the sensitive spectrum area.And then the grey correlation sensitive wavelength selection method was used to select wavelengths which were respectively 1 007,1 128,1 360,1 596,1 696,1 836,2 149 and 2 262 nm.Finally,by using the above sensitive wavelengths as input data,a soil total nitrogen prediction model was established based on the method of extreme learning machine and multiple linear regression.As a comparison,while using the traditional correlation analysis method to select the sensitive wavelengths,the results showed that R_c~2 of the soil total nitrogen forecast model established by using gray correlation-extreme learning machine was 0.913 4,and the prediction R_v~2 was 0.878 7.Its accuracy was higher than that of the traditional modeling method.It indicated that the gray correlation-extreme learning machine method had more obvious advantages especially in the prediction of low soil total nitrogen content.

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