Nondestructive Quantification of Foliar Chlorophyll in an Apple Orchard by Visible/Near-Infrared Reflectance Spectroscopy and Partial Least Squares

文献类型： 外文期刊

第一作者： Guo, Zhiming

作者： Guo, Zhiming;Zhao, Chunjiang;Huang, Wenqian;Guo, Jianhua;Guo, Zhiming;Wang, Yan'an

作者机构：

关键词： backward interval partial least squares; genetic algorithms; chlorophyll content; visible/near-infrared spectroscopy; apple orchard

期刊名称：SPECTROSCOPY LETTERS （ 影响因子：1.179； 五年影响因子：1.176 ）

ISSN： 0038-7010

年卷期： 2014 年 47 卷 6 期

页码：

收录情况： SCI

摘要： Chlorophylls respond rapidly to the current physiological status of a tree and reflect nutrient availability. Visible/near-infrared spectroscopy was attempted to determine foliar chlorophyll content in an apple orchard. Backward interval partial least squares and genetic algorithms were sequentially applied to select an optimized spectral interval and an optimized combination of spectral regions selected from informative regions in model calibration. Backward interval partial least squares was used to remove the noninformative regions, which significantly reduced the number of variables. The subsequent application of genetic algorithms-partial least squares to this reduced domain could lead to an efficient and refined model. The performance of the final model was back-evaluated according to root mean square error of calibration (RMSEC) and the correlation coefficient (R ( c )) in the calibration set, and was then tested by root mean square error of prediction (RMSEP) and the correlation coefficient (R ( p )) in the prediction set. The optimal backward interval partial least squares-genetic algorithms model was obtained with 5 partial least squares factors with 3 spectral regions and 71 variables selected. The measurement results of the final model were achieved as follows: RMSEC=0.26, R ( c )=0.91 in the calibration set; and RMSEP=0.22, R ( p )=0.91 in the prediction set. This experiment showed that visible/near-infrared spectroscopy and backward interval partial least squares-genetic algorithms are useful tools for nondestructively assessing foliar chlorophyll content and may have potential application for field assessments in decision-making and operational fertilizer management programs for apple orchards.

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