暗箱环境下采集柑橘叶片高光谱图像, 采用阈值法提取整叶有效光谱信息区域的平均光谱, 比对分析了柑橘叶片光谱信息不同预处理方法和光谱PLS、 BPNN和LS-SVM预测模型对叶绿素a、 叶绿素b和类胡萝卜素等光合色素含量的预测精度。 结果显示, 采用MSC对原始光谱进行预处理和LS-SVM建模对叶绿素a含量的预测效果较好, Rp达0.898 3, RMSEP为0.140 4; 采用SNV光谱预处理和LS-SVM模型对叶绿素b含量的预测其Rp为0.912 3, RMSEP为0.042 6; 采用MAS预处理和PLS模型对于类胡萝卜素含量预测的Rp和RMSEP分别为0.712 8和0.062 4。 结果表明: 采用高光谱图像信息可较好地进行柑橘叶片叶绿素a, 叶绿素b和类胡萝卜素等光合色素含量的预测, 为进一步研究柑橘叶片光合色素含量与组分构成的非损伤实时检测提供了依据。

The effective region was segmented from the hyperspectral image of citrus leaf by threshold method with the average spectrum extracted and used to describe the corresponding leaf. Based on the different spectral pre-processing methods, the prediction models of three photosynthetic pigments (i.e., chlorophyll a, chlorophyll b, and carotenoid) were calibrated by partial least squares (PLS), BP neural network (BPNN) and least square support vector machine (LS-SVM). The LS-SVM model for chlorophyll a was established based on multiplicative scatter correction (MSC), and the correlation coefficient (Rp) and the root mean square error of prediction (RMSEP) were 0.898 3 and 0.140 4, respectively. The LS-SVM model for chlorophyll b with Rp=0.912 3 and RMSEP=0.042 6, was established based on standard normal variable (SNV). The PLS model for carotenoid was established with Rp=0.712 8 and RMSEP=0.062 4 based on moving average smoothing (MAS), but the result was no better than the other two. The results illustrated that these three photosynthetic pigments could be nondestructively and real time estimated by hyperspectral image.