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http://dx.doi.org/10.5389/KSAE.2019.61.4.001

Estimation of Rice Grain Yield Distribution Using UAV Imagery  

Lee, KyungDo (National Institute of Agricultural Science, Rural Development Administration)
An, HoYong (National Institute of Agricultural Science, Rural Development Administration)
Park, ChanWon (National Institute of Agricultural Science, Rural Development Administration)
So, KyuHo (National Institute of Agricultural Science, Rural Development Administration)
Na, SangIl (National Institute of Agricultural Science, Rural Development Administration)
Jang, SuYong (Hanmaeum Farming Cooperation)
Publication Information
Journal of The Korean Society of Agricultural Engineers / v.61, no.4, 2019 , pp. 1-10 More about this Journal
Abstract
Unmanned aerial vehicle(UAV) can acquire images with lower cost than conventional manned aircraft and commercial satellites. It has the advantage of 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 yield distribution using UAV. In order to develop a technology for estimating the rice yield using UAV images, time series UAV aerial images were taken at the paddy fields and the data were compared with the rice yield of the harvesting area for two rice varieties(Singdongjin, Dongjinchal). Correlations between the vegetation indices and rice yield were ranged from 0.8 to 0.95 in booting period. Accordingly, rice yield was estimated using UAV-derived vegetation indices($R^2=0.70$ in Sindongjin, $R^2=0.92$ in Donjinchal). It means that the rice yield estimation using UAV imagery can provide less cost and higher accuracy than other methods using combine with yield monitoring system and satellite imagery. In the future, it will be necessary to study a variety of information convergence and integration systems such as image, weather, and soil for efficient use of these information, along with research on preparing management practice work standards such as pest control and nutrient use based on UAV image information.
Keywords
UAV; rice; yield; vegetation index; remote sensing;
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