title: [Hyperspectral estimation models of chlorophyll content in apple leaves].
abstract: 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 nm 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.
authors: College of Resources and Environment, Shandong Agricultural University, Tai'an 271018, China. liangshuang_3s@163.com
authors: Shuang
authors: S
authors: Liang
authors: Geng-xing
authors: GX
authors: Zhao
authors: Xi-cun
authors: XC
authors: Zhu
mesh_terms: D002734:Chlorophyll / Q000032:analysis*; D027845:Malus; D008962:Models, Theoretical; D018515:Plant Leaves / Q000737:chemistry*; D012044:Regression Analysis; D013057:Spectrum Analysis
chemical_list: D002734:Chlorophyll