基于高光谱成像技术的脐橙叶片的叶绿素含量及其分布测量

摘要

The chlorophyll content and distribution in the Gannan navel orange leaves were non-de-structively measured by competitive adaptive reweighted algorithm ( CARS ) and successive projec-tions algorithm ( SPA ) combined with hyperspectral imaging technology. 32 and 6 characteristic wavelengths were extracted by CARS and SPA, and then partial least squares ( PLS) was used for modeling quantitative analysis. The results show that SPA-PLS and CARS-PLS model can obtain bet-ter results than PLS model through the analysis of prediction of 37 samples. The prediction set corre-lation coefficients were 0. 90 and 0. 91, the root mean square error is 1. 53 and 1. 60 respectively. The chlorophyll content of each pixel was calculated with SPA-PLS model, then the chlorophyll dis-tribution map of navel orange leaves was visualized using imaging processing technology. Overall re-sults sufficiently demonstrate that the variable selection method combined with hyperspectral imaging technology can be used to measure the chlorophyll content and distribution in navel orange leaves.%为实现脐橙叶片叶绿素含量无损检测及其分布可视化表征,采用高光谱成像技术,结合自适应重加权算法( CARS)和连续投影算法( SPA),筛选特征光谱变量,进行脐橙叶片叶绿素含量及可视化分布研究。选取叶绿素测量位置的7×7矩形感兴趣区域,提取并计算脐橙叶片平均光谱。基于Kennard-ston方法,将148个脐橙叶片样品划分成建模集和预测集(111∶37)。采用CARS和SPA算法分别筛选出了32个和6个叶绿素特征光谱变量,用于建立偏最小二乘( PLS)回归模型。采用37个未参与建模的脐橙叶片样品评价模型的预测能力,经比较,CARS-PLS和SPA-PLS模型均优于变量筛选前的PLS模型,且CARS-PLS和SPA-PLS模型的预测能力几乎相同,其预测集相关系数分别为0.90和0.91,均方根误差分别为1.53和1.60。 SPA-PLS模型计算脐橙叶片每个像素点的叶绿素含量,经伪彩色变换,绘制了脐橙叶片叶绿素含量可视化分布图。实验结果表明：变量筛选方法结合高光谱成像技术,能够实现脐橙叶片叶绿素含量无损检测及叶绿素分布可视化表达,并简化了数学模型。