摘要
应用高光谱成像技术实现了油菜苗-花-角果整个生命期叶片氮含量的快速检测和氮素水平分布的可视化。采集三个生长时期共计420个叶片样本的高光谱图像信息(380~1 030nm),提取图像中感兴趣区域的平均光谱数据,经过不同光谱预处理后,利用连续投影算法(SPA)选择特征波长,将提取的12个特征波长(467,557,665,686,706,752,874,879,886,900,978和995nm)作为自变量,叶片氮含量作为因变量,分别建立偏最小二乘法(PLS)和最小二乘-支持向量机(LS-SVM)模型。SPA-PLS和SPA-LS-SVM模型对叶片氮含量的预测相关系数RP分别为0.807和0.836,预测均方根误差RMSEP分别为0.387和0.358。高光谱图像中的每一个像素点都有对应的光谱反射值,利用结构简单、更易提取回归系数的SPA-PLS模型,快速计算出12个特征波长下高光谱图像中每个像素点对应的氮含量预测值,结合像素点的空间位置生成氮素浓度的叶面分布图。可视化分布图详细且直观的反应出同一叶片内部或不同叶片之间氮含量的差异。结果表明,应用高光谱成像技术分析整个油菜生长期的叶片氮含量及其可视化分布是可行的。
Visible and near infrared(Vis-NIR)hyperspectral imaging system was carried out to rapidly determinate the content and estimate the distribution of nitrogen(N)in oilseed rape leaves.Hyperspectral images of 420 leaf samples were acquired at seedling,flowering and pod stages.The spectral data of rape leaves were extracted from the region of interest(ROI)in the wavelength range of 380~1 030 nm.Different spectra preprocessing including Savitzky-Golay smoothing(SG),standard normal variate(SNV),multiplicative scatter correction(MSC),first and second derivatives were applied to improve the signal to noise ratio.Among 471 wavelengths,only twelve wavelengths(467,557,665,686,706,752,874,879,886,900,978 and 995nm)were selected by successive projections algorithm(SPA)as the effective wavelengths for N prediction.Based on these effective wavelengths,partial least squares(PLS)and least-squares support vector machines(LS-SVM)calibration models were established for the determination of N content.Reasonable estimation accuracy was obtained,with RPof 0.807 and RMSEP of0.387 by PLS and RPof 0.836 and RMSEP of 0.358 by LS-SVM,respectively.Considering the simple structure and satisfying results of PLS model,SPA-PLS model was used to generate the distribution maps of N content in rape leaves.The concentrations of N were calculated at each pixel of hyperspectral images at the selected effective wavelengths by inputting its corresponding spectrum into the established SPA-PLS model.Different colour represented the change in N content in the rape leaves under different fertilizer treatments.By including all pixels within the selected ROI,the average N status can be displayed in more detail and visualised.The visualization of N distribution could be helpful to understanding the change in N content in rape leaves during rape growth period and facilitate discovering the difference of N content within one sample as well as among the samples from different fertilising plots.The overall results revealed that hyperspectral imaging is a promising
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2014年第9期2513-2518,共6页
Spectroscopy and Spectral Analysis
基金
国家高技术研究发展计划(863计划)项目(2013AA102405
2011AA100705)
教育部高等学校博士学科点专项科研基金项目(20130101110104)
宁波市科技局重点项目(2011C11024)
中央高校基本科研业务费专项资金项目(2014FZA6005)资助
关键词
高光谱成像
油菜
氮素分布
偏最小二乘法
连续投影算法
Hyperspectral imaging
Oilseed rape(Brassica napus L.)
Distribution of nitrogen
Partial least square
Successive projections algorithm
