高光谱成像技术的油菜叶片氮含量及分布快速检测

摘要

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 wave-length range of 380~1 030 nm .Different spectra preprocessing including Savitzky-Golay smoothing (SG) ,standard normal vari-ate (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 995 nm) were selected by successive projections algorithm (SPA) as the effective wavelengths for N prediction .Based on these effec-tive wavelengths ,partial least squares(PLS) and least-squares support vector machines (LS-SVM ) calibration models were es-tablished for the determination of N content .Reasonable estimation accuracy was obtained ,with RP of 0.807 and RMSEP of 0.387 by PLS and RP of 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 concentra-tions of N were calculated at each pixel of hyperspectral images at the selected effective wavelengths by inputting its correspond-ing 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 de-tail 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 technique to detect N con-tent and distribution within oilseed rape leaves rapidly and nondestructively .%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（380～1030 nm ），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995 nm ）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。