数字农科院2.0

Determination of Photosynthetic Pigments in Citrus Leaves Based on Hyperspectral Images Datas

文献类型： 外文期刊

作者： Tian Xi;Liao Qiu-hong;Tian Xi;He Shao-lan;Lu Qiang;Yi Shi-lai;Xie Rang-jin;Zheng Yong-qiang;Liao Qiu-hong;Deng Lie;He Shao-lan;Lu Qiang;Yi Shi-lai;Xie Rang-jin;Zheng Yong-qiang;Deng Lie

作者机构：

关键词： Citrus leaf;Photosynthetic pigment;Hyperspectral imaging;BP neural network;Least square support vector machines

期刊名称： SPECTROSCOPY AND SPECTRAL ANALYSIS

ISSN： 1000-0593

年卷期： 2014 年 34 卷 9 期

页码：

收录情况： SCI

摘要： 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 (R-p) and the root mean square error of prediction (RMSEP) were 0.8983 and 0.1404, respectively. The LS-SVM model for chlorophyll b with R-p=0.9123 and RMSEP=0.0426, was established based on standard normal variable (SNV). The PLS model for carotenoid was established with R-p=0.7128 and RMSEP=0.0624 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.

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