한국농공학회논문집 (Journal of The Korean Society of Agricultural Engineers)

  • 제62권4호
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  • Pages.75-86
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  • 2020
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  • 1738-3692(pISSN)
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  • 2093-7709(eISSN)
한국농공학회 (The Korean Society of Agricultural Engineers)
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무인비행체 영상 기반 연차 간 벼 생육 및 흰잎마름병 병해 추정

Yearly Estimation of Rice Growth and Bacterial Leaf Blight Inoculation Effect Using UAV Imagery

  • Lee, KyungDo (National Institute of Agricultural Sciences, Rural Development Administration) ;
  • Kim, SangMin (National Institute of Crop Science, Rural Development Administration) ;
  • An, HoYong (National Institute of Agricultural Sciences, Rural Development Administration) ;
  • Park, ChanWon (National Institute of Agricultural Sciences, Rural Development Administration) ;
  • Hong, SukYoung (National Institute of Agricultural Sciences, Rural Development Administration) ;
  • So, KyuHo (National Institute of Agricultural Sciences, Rural Development Administration) ;
  • Na, SangIl (National Institute of Agricultural Sciences, Rural Development Administration)
  • 투고 : 2020.05.12
  • 심사 : 2020.07.21
  • 발행 : 2020.07.31
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초록

The purpose of this study is to develop a technology for estimating rice growth and damage effect according to bacterial leaf blight using UAV multi-spectral imagery. For this purpose, we analyzed the change of aerial images, rice growth factors (plant height, dry weight, LAI) and disease effects according to disease occurrence by using UAV images for 3 rice varieties (Milyang23, Sindongjin-byeo, Saenuri-byeo) from 2017 to 2018. The correlation between vegetation index and rice growth factor during vegetative growth period showed a high value of 0.9 or higher each year. As a result of applying the growth estimation model built in 2017 to 2018, the plant height of Milyang23 showed good error withing 10%. However, it is considered that studies to improve the accuracy of other items are needed. Fixed wing unmanned aerial photographs were also possible to estimate the damage area after 2 to 4 weeks from inoculation. Although sensing data in the multi-spectral (Blue, Green, Red, NIR) band have limitations in early diagnosis of rice disease, for rice varieties such as Milyang23 and Sindongjin-byeo, it was possible to construct the equation of infected leaf area ratio and rice yield estimation using UAV imagery in early and mid-September with high correlation coefficient of 0.8 to 0.9. The results of this study are expected to be useful for farming and policy support related to estimating rice growth, rice plant disease and yield change based on UAV images.

키워드

참고문헌

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