: Unmanned Aerial Vehicle (UAV) imagery are being assessed for analyzing within field spatial variability for agricultural precision management, because UAV imagery may be acquired quickly during critical periods of rapid crop growth. This study refers to the derivation of barley and wheat growth prediction equation by using UAV derived vegetation index. UAV imagery was taken on the test plots six times from late February to late June during the barley and wheat growing season. The field spectral reflectance during growing period for the 5 variety (Keunal-bori, Huinchalssal-bori, Saechalssal-bori, Keumkang and Jopum) were measured using ground spectroradiometer and three growth parameters, including plant height, shoot dry weight and number of tiller were investigated for each ground survey. Among the 6 Vegetation Indices (VI), the RVI, NDVI, NGRDI and GLI between measured and image derived showed high relationship with the coefficient of determination respectively. Using the field investigation data, the vegetation indices regression curves were derived, and the growth parameters were tried to compare with the VIs value.

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Unmanned Aerial Vehicle (UAV)s have a high resolution of around 10 cm, and can acquire images with lower cost when we want. The purpose of this study was to estimate the winter wheat nitrogen content, biomass and grain yield using UAV image. We collected the time series UAV aerial images at the upland and the data were compared with the wheat nitrogen contents, biomass and yield in the experimental sites. Correlation coefficient (R) between the vegetation indices (NDVI, GNDVI, RVI, GRVI, NDRE) and wheat nitrogen contents were more than 0.8 from early-April to late-April. Correlation coefficient (R) between the vegetation indices (NDVI, GNDVI, RVI, GRVI, NDRE) and wheat biomass, grain yield, and crude protein were from 0.7 to 0.8 from early-April to late-April. Using these results, we were able to make the distribution map of the wheat nitrogen, biomass and yield. In the future, it will be necessary to study to recommend supplemental fertilization to ensure the wheat yield using UAV images information.

Recent advanced UAV (Unmanned Aerial Vehicle) technology supply new opportunities for estimating crop condition using high resolution imagery. The objective of this study was to analyze the change of vegetation index in UAV imagery according to sun altitude. This study was conducted using a fixed-wing UAV, called Ebee, with Cannon S110 camera from November 2017 to September 2018 in the grass experiment of National Institute of Agricultural Sciences on a clear day. As a result, the NDVI (Normalized Difference Vegetation Index) of UAV imagery decreased after 9 a.m. and showed a minimum value at 13 a.m. and increased since then. The solar zenith angle and the NDVI of UAV imagery showed a positive linear relationship. Therefore, in order to quantitatively compare and analyze the time series vegetation index, it is necessary to establish a UAV flight plan considering the change of solar zenith angle. It is thought that it will be necessary to examine cloudy days with various crop in the future.

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