무인기 기반 RGB 영상을 이용한 동계작물 바이오매스 평가 모델 개발 = Development of Biomass Evaluation Model of Winter Crop Using RGB Imagery Based on Unmanned Aerial Vehicle

다국어 초록 (Multilingual Abstract)

In order to optimize the evaluation of biomass in crop monitoring, accurate and timely data of the crop-field are required. Evaluating above-ground biomass helps to monitor crop vitality and to predict yield. 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 reports on the development of remote sensing techniques for evaluating the biomass of winter crop. Specific objective was to develop statistical models for estimating the dry weight of barley and wheat using a Excess Green index (E × G) based Vegetation Fraction (VF) and a Crop Surface Model (CSM) based Plant Height (PH) value. As a result, the multiple linear regression equations consisting of three independent variables (VF, PH, and VF × PH) and aboveground dry weight provided good fits with coefficients of determination (R²) ranging from 0.86 to 0.99 with 5 cultivars. In the case of the barley, the coefficient of determination was 0.91 and the root mean squared error of measurement was 102.09 g/㎡. And for the wheat, the coefficient of determination was 0.90 and the root mean squared error of measurement was 110.87 g/㎡. Therefore, it will be possible to evaluate the biomass of winter crop through the UAV image for the crop growth monitoring.

참고문헌 (Reference)

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