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Development of Biomass Evaluation Model of Winter Crop Using RGB Imagery Based on Unmanned Aerial Vehicle

무인기 기반 RGB 영상을 이용한 동계작물 바이오매스 평가 모델 개발

  • Na, Sang-il (National Institute of Agricultural Sciences, Rural Development Administration) ;
  • Park, Chan-won (National Institute of Agricultural Sciences, Rural Development Administration) ;
  • So, Kyu-ho (National Institute of Agricultural Sciences, Rural Development Administration) ;
  • Ahn, Ho-yong (National Institute of Agricultural Sciences, Rural Development Administration) ;
  • Lee, Kyung-do (National Institute of Agricultural Sciences, Rural Development Administration)
  • 나상일 (농촌진흥청 국립농업과학원) ;
  • 박찬원 (농촌진흥청 국립농업과학원) ;
  • 소규호 (농촌진흥청 국립농업과학원) ;
  • 안호용 (농촌진흥청 국립농업과학원) ;
  • 이경도 (농촌진흥청 국립농업과학원)
  • Received : 2018.07.01
  • Accepted : 2018.08.01
  • Published : 2018.10.31

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 ($E{\times}G$) 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 $VF{\times}PH$) and above-ground dry weight provided good fits with coefficients of determination ($R^2$) 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 $102.09g/m^2$. And for the wheat, the coefficient of determination was 0.90 and the root mean squared error of measurement was $110.87g/m^2$. Therefore, it will be possible to evaluate the biomass of winter crop through the UAV image for the crop growth monitoring.

Acknowledgement

Supported by : 농촌진흥청

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