한국농림기상학회지 (Korean Journal of Agricultural and Forest Meteorology)

  • 제23권4호
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  • Pages.340-348
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  • 2021
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  • 1229-5671(pISSN)
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  • 2288-1859(eISSN)
한국농림기상학회 (Korean Society of Agricultural and Forest Meteorology)
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RGB 작물 생육지수를 활용한 콩 한발 스트레스 판별기술 평가

Detection of Drought Stress in Soybean Plants using RGB-based Vegetation Indices

  • 상완규 (농촌진흥청 국립식량과학원) ;
  • 김준환 (농촌진흥청 국립식량과학원) ;
  • 백재경 (농촌진흥청 국립식량과학원) ;
  • 권동원 (농촌진흥청 국립식량과학원) ;
  • 반호영 (농촌진흥청 국립식량과학원) ;
  • 조정일 (농촌진흥청 국립식량과학원) ;
  • 서명철 (농촌진흥청 국립식량과학원)
  • 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)
  • 투고 : 2021.11.03
  • 심사 : 2021.11.22
  • 발행 : 2021.12.30
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초록

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.

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.

키워드

과제정보

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

참고문헌

  1. Araus, J. L., S. C. Kefauver, M. Z. Allah, M. S. Olsen, and J. E. Cairns, 2018: Translating highthroughput phenotyping into genetic gain. Trends in Plant Science 23, 451-466. https://doi.org/10.1016/j.tplants.2018.02.001
  2. Garcia-Ruiz, F., S. Sankaran, J. M. Maja, W. S. Lee, J. Rasmussen, and R. Ehsani, 2013: Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees. Computers and Electronics in Agriculture 91, 106-115. https://doi.org/10.1016/j.compag.2012.12.002
  3. Lee, Y. H., W. G. Sang, J. K. Baek, J. H. Kim, J. I. Cho, and M. C. Seo, 2020: Low-cost assessment of canopy light interception and leaf area in soybean canopy cover using RGB color images. Korean Journal of Agricultural and Forest Meteorology 22(1), 13-19. https://doi.org/10.5532/KJAFM.2020.22.1.13
  4. Li, L., Q, Zhabg, and D. Huang, 2014: A review of imaging techniques for plant phenotyping. Sensors 14, 20078-20111. https://doi.org/10.3390/s141120078
  5. Nasirzadehdizaji, R., F. B. Sanli, S. Abdikan, Z. Cakir, A. Sekertekin, and M. Ustuner, 2019: Sensitivity analysis of multi-temporal sentinel-1 SAR parameters to crop height and canopy coverage. Applied Science 9, 655. https://doi.org/10.3390/app9040655
  6. Richardson, A. D., S. P. Duigan, and G. P. Berlyn, 2002: An evaluation of noninvasive methods to estimate foliar chlorophyll content. New Phytologist 153, 185-194. https://doi.org/10.1046/j.0028-646X.2001.00289.x
  7. Sang, W. G., 2020: Impact of climate change on growth, canopy photosynthesis and metabolic physiology of soybean in Southern Korea. Jeonbuk National University.
  8. Smith, G. M., and E. J. Milton, 1999: The use of the empirical line method to calibrate remotely sensed data to reflectance. International Journal of Remote Sensing 20(13), 2653-2662. https://doi.org/10.1080/014311699211994
  9. Swain, K. C., S. J. Thomson, and H. P. W. Jayasuriya, 2010: Adoption of an unmanned helicopter for low-altitude remote sensing to estimate yield and total biomass of a rice crop. American Society of Agricultural and Biological Engineers 53(1), 21-27.
  10. Torres-Sanchez, J., J. M. Pena, A. I. de Castro, and F. Lopez-Granados, 2014: Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV. Computers and Electronics in Agriculture 103, 104-113. https://doi.org/10.1016/j.compag.2014.02.009
  11. Wahid, A., and E. Rasul, 2005: Photosynthesis in leaf, stem, flower and fruit, in: Pessarakil M. (Ed.), Handbook of Photosynthesis, 2nd ed., CRC Press, Florid, 104-109.
  12. Wang, C., and S. W. Myint, 2015: A Simplified Empirical Line Method of Radiometric Calibration for Small Unmanned Aircraft Systems-Based Remote Sensing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8(5), 1876-1885. https://doi.org/10.1109/JSTARS.2015.2422716
  13. Yun, H., 2017: Development of Remote Sensing Technology for Growth Estimation of White Radish and Napa Cabbage using UAV and RGB Camera. Seoul National University.
  14. Zarco-Tejada, P. J., V. Gonzalez-Dugo, L. E. Williams, L. Suarez, J. A. Berni, D. Goldhamer, and E. Fereres, 2013: A PRI-based water stress index combining structural and chlorophyll effects: Assessment using diurnal narrow-band airborne imagery and the CWSI thermal index. Remote Sensing of Environment 138, 38-50. https://doi.org/10.1016/j.rse.2013.07.024