苹果树叶片叶绿素含量高光谱估测模型研究

摘要

叶片叶绿素含量是评估果树长势和产量的重要参数,实现快速、无损、精确的叶绿素含量估测具有重要意义.本研究以山东农业大学苹果园为试验区,采用高光谱分析技术探索苹果树叶片叶绿素含量的估测方法.通过分析叶片高光谱曲线特征,对原始光谱分别进行一阶微分、红边位置以及叶面叶绿素指数(LCI)变换,分别将其与叶绿素含量进行相关分析及回归分析,建立叶绿素含量估测模型并进行检验,从中筛选出精度最高的模型.结果显示,以LCI为变量的估测模型以及以一阶微分521和523 nm组合为变量的估测模型拟合精度最高,决定系数R2分别为0.845和0.839,均方根误差RMSE分别为2.961和2.719,相对误差RE％分别为4.71％和4.70％.因此LCI及一阶微分是估测苹果树叶片叶绿素含量的重要指标.该模型对指导苹果树栽培生产具有积极意义.%The present study chose the apple orchard of Shandong Agricultural University as the study area to explore the method of apple leaf chlorophyll content estimation by hyperspectral analysis technology. Through analyzing the characteristics of apple leaves' hyperspectral curve, transforming the original spectral into first derivative, red edge position and leaf chlorophyll index (LCI) respectively, and making the correlation analysis and regression analysis of these variables with the chlorophyll content to establish the estimation models and test to select the high fitting precision models. Results showed that the fitting precision of the estimation model with variable of LCI and the estimation model with variable of the first derivative in the band of 521 and 523 ran was the highest The coefficients of determination R2 were 0. 845 and 0. 839, the root mean square errors RMSE were 2. 961 and 2. 719, and the relative errors RE% were 4. 71% and 4. 70%, respectively. Therefore LCI and the first derivative are the important index for apple leaf chlorophyll content estimation. The models have positive significance to guide the production of apple cultivation.