무인비행체 영상을 활용한 벼 수량 분포 추정
Estimation of Rice Grain Yield Distribution Using UAV Imagery
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참고문헌
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초록
Unmanned aerial vehicle(UAV) can acquire images with lower cost than conventional manned aircraft and commercial satellites. It has the advantageof acquiring high-resolution aerial images covering in the field area more than 50 ha. The purposes of this study is to develop the rice grain yielddistribution using UAV. In order to develop a technology for estimating the rice yield using UAV images, time series UAV aerial images were takenat the paddy fields and the data were compared with the rice yield of the harvesting area for two rice varieties(Singdongjin, Dongjinchal). Correlationsbetween the vegetation indices and rice yield were ranged from 0.8 to 0.95 in booting period. Accordingly, rice yield was estimated using UAV-derivedvegetation indices(R2=0.70 in Sindongjin, R2=0.92 in Donjinchal). It means that the rice yield estimation using UAV imagery can provide less cost andhigher accuracy than other methods using combine with yield monitoring system and satellite imagery. In the future, it will be necessary to study avariety of information convergence and integration systems such as image, weather, and soil for efficient use of these information, along with researchon preparing management practice work standards such as pest control and nutrient use based on UAV image information.
Abstract
Unmanned aerial vehicle(UAV) can acquire images with lower cost than conventional manned aircraft and commercial satellites. It has the advantageof acquiring high-resolution aerial images covering in the field area more than 50 ha. The purposes of this study is to develop the rice grain yielddistribution using UAV. In order to develop a technology for estimating the rice yield using UAV images, time series UAV aerial images were takenat the paddy fields and the data were compared with the rice yield of the harvesting area for two rice varieties(Singdongjin, Dongjinchal). Correlationsbetween the vegetation indices and rice yield were ranged from 0.8 to 0.95 in booting period. Accordingly, rice yield was estimated using UAV-derivedvegetation indices(R2=0.70 in Sindongjin, R2=0.92 in Donjinchal). It means that the rice yield estimation using UAV imagery can provide less cost andhigher accuracy than other methods using combine with yield monitoring system and satellite imagery. In the future, it will be necessary to study avariety of information convergence and integration systems such as image, weather, and soil for efficient use of these information, along with researchon preparing management practice work standards such as pest control and nutrient use based on UAV image information.
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