| 基于灰度关联-极限学习机的土壤全氮预测 |
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| 引用本文： | 周鹏,杨玮,李民赞,郑立华,陈玉青.基于灰度关联-极限学习机的土壤全氮预测[J].农业机械学报,2017,48(S1):271-276. |
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| 作者姓名： | 周鹏 杨玮 李民赞 郑立华 陈玉青 |
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| 作者单位： | 中国农业大学,中国农业大学,中国农业大学,中国农业大学,中国农业大学 |
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| 基金项目： | 国家重点研发计划项目（2017YFD0201500-2017YFD0201501、2016YFD0700300-2016YFD0700304） |
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| 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1007、1128、1360、1596、1696、1836、2149、2262nm。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R2c为0.9134，预测决定系数R2v为0.8787，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。
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| 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 |
| 收稿时间： | 2017/7/10 0:00:00 |
Soil Total Nitrogen Content Prediction Based on Gray Correlation-extreme Learning Machine |
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| ZHOU Peng,YANG Wei,LI Minzan,ZHENG Lihua and CHEN Yuqing.Soil Total Nitrogen Content Prediction Based on Gray Correlation-extreme Learning Machine[J].Transactions of the Chinese Society of Agricultural Machinery,2017,48(S1):271-276. |
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| Authors: | ZHOU Peng YANG Wei LI Minzan ZHENG Lihua and CHEN Yuqing |
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| Institution: | China Agricultural University,China Agricultural University,China Agricultural University,China Agricultural University and China Agricultural University |
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| Abstract: | In order to overcome the influences of multi-collinearity and absorbance non-linearity in near-infrared 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 1007, 1128, 1360, 1596, 1696, 1836, 2149 and 2262nm. 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 R2c of the soil total nitrogen forecast model established by using gray correlation-extreme learning machine was 0.9134, and the prediction R2v was 0.8787. 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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| Keywords: | near infrared spectroscopy wavelength selection gray correlation soil total nitrogen extreme learning machine |
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