Journal of The Korean Society of Agricultural Engineers (한국농공학회논문집)

The Korean Society of Agricultural Engineers (한국농공학회)
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Estimation of Rice Grain Yield Distribution Using UAV Imagery

무인비행체 영상을 활용한 벼 수량 분포 추정

  • 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)
  • Received : 2019.04.30
  • Accepted : 2019.05.20
  • Published : 2019.07.31
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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

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Fig. 1 Location of study area and sampling sites

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Fig. 2 Aerial photos of study site and sample locations

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Fig. 3 Rice yield of study sample sites

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Fig. 4 Variations of UAV imagery vegetation indices on study sample sites

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Fig. 5 Correlation coefficient between vegetation index and rice yield

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Fig. 6 Relationship between rice (Sindongji) yield and vegetation indices

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Fig. 7 Relationship between rice (Dongjinchal) yield and vegetation indices

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Fig. 8 Scatter plot of rice grain yield estimation model

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Fig. 9 Vegetation index (GNDVI) in booting period and yield distribution map using UAV imagery on Sindongjin in Site #1, #2

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Fig. 10 Vegetation index (GNDVI) in booting period and yield distribution map using UAV imagery on Dongjinchal in Site #3, #4,#5

Table 1 UAV image collecting dates and flight information

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Table 2 Vegetation indices related to crop growth monitoring

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Table 3 Descriptive statistics of paddy rice grain yield

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