TITLE:
Hyperspectral Inversion of Potassium Content in Apple Leaves Based on Vegetation Index
AUTHORS:
Xiaoyan Guo, Xicun Zhu, Cheng Li, Yu Wei, Xinyang Yu, Gengxing Zhao, Houxing Sun
KEYWORDS:
Hyperspectral Inversion, Vegetation Index, Apple Tree Leaf, Potassium Content, Random Forest Regression Model
JOURNAL NAME:
Agricultural Sciences,
Vol.8 No.8,
August
21,
2017
ABSTRACT: The aim of this study is to establish the estimation model of potassium content in apple leaves by using vegetation index. A total of 96 fresh apple leaves were collected from 24 orchards in Qixia County, Shandong Province. The spectral reflectance of the leaves was measured by ASD FieldSpec4. The difference vegetation index (DVI), ratio vegetation index (RVI) and normalized vegetation index (NDVI) were used to make the contour map through Matlab platform, and the combination of high correlation wavelength was selected to establish the random forest (RF) regression model of potassium content. The hyperspectral reflectance increased with the increase of leaf potassium content. The correlation between DVI and the content of potassium is higher than NDVI and RVI. The optimal vegetation index was DVI (364,740), the correlation coefficient was 0.5355. The random forest regression model established with DVI selected vegetation index was the best. R2 was 0.8995, RMSE and RE% were 0.0791 and 0.0617 respectively. Using DVI to establish the random forest regression model to reverse the potassium content of apple leaves has achieved good results. It is important to determine the growth status of apple in hyperspectral and to determine the potash fertilizer of apple trees.