Korean Journal of Agricultural and Forest Meteorology (한국농림기상학회지)

Korean Society of Agricultural and Forest Meteorology (한국농림기상학회)
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Detection of Drought Stress in Soybean Plants using RGB-based Vegetation Indices

RGB 작물 생육지수를 활용한 콩 한발 스트레스 판별기술 평가

  • Sang, Wan-Gyu (National Institute of Crop Science, Rural Development Administration) ;
  • Kim, Jun-Hwan (National Institute of Crop Science, Rural Development Administration) ;
  • Baek, Jae-Kyeong (National Institute of Crop Science, Rural Development Administration) ;
  • Kwon, Dongwon (National Institute of Crop Science, Rural Development Administration) ;
  • Ban, Ho-Young (National Institute of Crop Science, Rural Development Administration) ;
  • Cho, Jung-Il (National Institute of Crop Science, Rural Development Administration) ;
  • Seo, Myung-Chul (National Institute of Crop Science, Rural Development Administration)
  • 상완규 (농촌진흥청 국립식량과학원) ;
  • 김준환 (농촌진흥청 국립식량과학원) ;
  • 백재경 (농촌진흥청 국립식량과학원) ;
  • 권동원 (농촌진흥청 국립식량과학원) ;
  • 반호영 (농촌진흥청 국립식량과학원) ;
  • 조정일 (농촌진흥청 국립식량과학원) ;
  • 서명철 (농촌진흥청 국립식량과학원)
  • Received : 2021.11.03
  • Accepted : 2021.11.22
  • Published : 2021.12.30
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Abstract

Continuous monitoring of RGB (Red, Green, Blue) vegetation indices is important to apply remote sensing technology for the estimation of crop growth. In this study, we evaluated the performance of eight vegetation indices derived from soybean RGB images with various agronomic parameters under drought stress condition. Drought stress influenced the behavior of various RGB vegetation indices related soybean canopy architecture and leaf color. In particular, reported vegetation indices such as ExGR (Excessive green index minus excess red index), Ipca (Principal Component Analysis Index), NGRDI (Normalized Green Red Difference Index), VARI (Visible Atmospherically Resistance Index), SAVI (Soil Adjusted Vegetation Index) were effective tools in obtaining canopy coverage and leaf chlorophyll content in soybean field. In addition, the RGB vegetation indices related to leaf color responded more sensitively to drought stress than those related to canopy coverage. The PLS-DA (Partial Squares-Discriminant Analysis) results showed that the separation of RGB vegetation indices was distinct by drought stress. The results, yet preliminary, display the potential of applying vegetation indices based on RGB images as a tool for monitoring crop environmental stress.

본 연구는 콩의 한발 스트레스 판별에 대하여 RGB 영상에 기반한 작물 생육 지수의 적용 가능성과 한계점을 구명하기 위해 수행되었다. RGB 영상에서 추출한 생육 지수들과 한발 스트레스에 반응하는 대표적인 표현형 지표들(군락 피복도, 엽면적, 엽록소 함량 등)과의 높은 상관관계를 통해 영상 기반 생육 진단 모델개발의 가능성을 확인할 수 있었다. 다만 판별의 정확도와 해상도를 개선시키기 위해서는 향후 다양한 재배조건에서 지속적인 성능 평가가 이루어져야 할 것이다. 본 연구의 결과는 향후 RGB 영상을 활용한 콩환경 스트레스 판별에 있어서 영상 전처리, 영상 분석방법, 생육 지수 정량화 기술 개발에 도움을 줄 수 있을 것이며, 개발된 생육 인자 예측 모델은 환경 스트레스 조기 진단을 통한 영농 의사결정 지원 모델의 개발에 기여할 수 있을 것으로 판단된다.

Keywords

Acknowledgement

본 논문은 농촌진흥청 국립식량과학원 농업과학기술 연구개발사업(과제번호: PJ01476801)의 지원에 의해 이루어진 것임.

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