The aim of this study is to establish the estimation model of potassium content\nin apple leaves by using vegetation index. A total of 96 fresh apple leaves\nwere collected from 24 orchards in Qixia County, Shandong Province. The\nspectral reflectance of the leaves was measured by ASD FieldSpec4. The difference\nvegetation index (DVI), ratio vegetation index (RVI) and normalized\nvegetation index (NDVI) were used to make the contour map through Matlab\nplatform, and the combination of high correlation wavelength was selected to\nestablish the random forest (RF) regression model of potassium content. The\nhyperspectral reflectance increased with the increase of leaf potassium content.\nThe correlation between DVI and the content of potassium is higher\nthan NDVI and RVI. The optimal vegetation index was DVI (364,740), the\ncorrelation coefficient was 0.5355. The random forest regression model established\nwith DVI selected vegetation index was the best. R2 was 0.8995, RMSE\nand RE% were 0.0791 and 0.0617 respectively. Using DVI to establish the\nrandom forest regression model to reverse the potassium content of apple\nleaves has achieved good results. It is important to determine the growth status\nof apple in hyperspectral and to determine the potash fertilizer of apple\ntrees.
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