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Unmanned Aerial Vehicle (UAV) imagery are being assessed for analyzing within field spatial variability for agricultural precision management, because unmanned aerial vehicle imagery may be acquired quickly during critical periods of rapid crop growth. The objective of this study was to evaluate the use of unmanned aerial vehicle for the monitoring barley growth. Unmanned aerial vehicle imagery obtained from middle February to late June in Wanju, Jeollabuk-do. Unmanned aerial vehicle imagery corrected geometrically and atmospherically to calculate normalized difference vegetation index (NDVI). We analyzed the relationships between NDVI UAV of barley and biophysical measurements such as plant height, number of tiller and shoot dry weight over an entire barley growth period. The similar trend between NDVI UAV and growth parameters was shown. Correlation analysis between NDVI UAV and barley growth parameters revealed that NDVI UAV was highly correlated with shoot dry weight (r=0.932) and plant height (r=0.879). According to the relationship among growth parameters and NDVI UAV , the temporal variation of NDVIUAV was significant to interpret barley growth. The spatial distribution map of barley growth was in strong agreement with the field measurements in terms of geographical variation and relative numerical values when NDVI UAV was applied to power function. From these results, NDVI UAV can be used as a new tool for monitoring barley growth.

For more than 50 years, satellite images have been used to monitor crop growth. Currently, 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 highland Kimchi cabbage growth prediction equation by using UAV derived normalized difference vegetation index (NDVI) and agro-meteorological factors. Anbandeok area in Gangneung, Gangwon-do, Korea is one of main producing district of highland Kimchi cabbage. UAV imagery was taken on the Anbandeok twelve times from early June to early September during the highland Kimchi cabbage growing season. Meanwhile, field reflectance spectra and three plant growth parameters, including plant height (P.H.), leaf height (L.H.) and leaf number (L.N.), were measured for about 40 plants (ten plants per plot) for each ground survey. The NDVIField for each of the 40 plants was measured using an active plant growth sensor (Crop CircleTM) at the same time. Six agro-meteorological factors include average temperature; maximum temperature; minimum temperature; accumulated temperature; rainfall and irradiation. The multiple linear regression models were suggested by using stepwise regression in the extraction of independent variables. Of the output independent variables of the stepwise regression, NDVIUAV performs best (r=0.933). And correlation coefficients of highland Kimchi cabbage growth parameters, accumulated temperature, irradiation and rainfall showed positive relationship; the results were respectively 0.931, 0.926, 0.893 (p