基于高光谱成像技术的油菜SPAD值空间分布预测及最佳测量叶位
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国家自然科学基金项目(31501222,41201364); 中央高校基本科研业务费专项(2017JC038,2015BQ026)


Hyperspectral imaging technology based prediction of spatial distribution of SPAD value of rapeseed and optimal measurement of leaf position 
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    摘要:

    采集不同氮素处理水平下的油菜植株不同叶位和叶片部位的高光谱数据、SPAD值和叶绿素含量实测值,在筛选原始高光谱数据预处理方法的基础上,比较基于偏最小二乘(partial least squares,PLS)模型和最小二乘-支持向量机(least squares support vector machine,LS-SVM)的SPAD预测模型。结果表明,基于标准正态变量校正(standard normal variate,SNV)预处理方法的LS-SVM模型(SNV-LS-SVM)为最佳高光谱-SPAD预测模型,可准确预测油菜叶片SPAD值空间分布和可视化结果。基于SPAD空间分布结果提取不同叶位和叶片部位的SPAD值,将其与对应植株和叶片位置的实测叶绿素含量进行相关性分析,结果显示,油菜SPAD值最佳测量叶位为顶四叶的顶部。

    Abstract:

    The hyperspectral images,SPAD and chlorophyll content from different part of leaf of different rapeseed plants under different nitrogen levels were collected.The methods of preprocessing hyperspectral data were calculated and compared.The PLS (partial least squares) and LS-SVM (least squares support vector machine) methods were used to build prediction model of rapeseed leaf SPAD.The results showed that the result of LS-SVM prediction model based on the SNV (standard normal variate) processing method named as SNV-LS-SVM was the best.The map of rapeseed leaf spatial distribution SPAD was constructed according the best prediction model.The SPAD value extracted from SPAD map of different rapeseed plants and positions was analyzed with chlorophyll content.The results showed that leaf on the top part of fourth rapeseed plant was the optimal measurement position.This paper combines the advantage of hyperspectral imaging technique and the non-destructive SPAD measurement method.The methods proposed realized the predicting SPAD spatial distribution of rapeseed leaf and the optimizing measurement position identification.It will provide a theoretical basis and methodological guidance for efficiently detecting chlorophyll content in rapeseed plant.

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赵琨,王珺珂,王楚锋,谢田晋,张建.基于高光谱成像技术的油菜SPAD值空间分布预测及最佳测量叶位[J].华中农业大学学报,2018,37(04):78-84

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  • 在线发布日期: 2018-07-09
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