Korean Journal of Agricultural and Forest Meteorology (한국농림기상학회지)

Korean Society of Agricultural and Forest Meteorology (한국농림기상학회)
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Estimation of Fresh Weight, Dry Weight, and Leaf Area Index of Soybean Plant using Multispectral Camera Mounted on Rotor-wing UAV

회전익 무인기에 탑재된 다중분광 센서를 이용한 콩의 생체중, 건물중, 엽면적 지수 추정

  • Jang, Si-Hyeong (Department of Agro-System Engineering, GyeongSang National University (Institute of Agriculture & Life Science)) ;
  • Ryu, Chan-Seok (Department of Agro-System Engineering, GyeongSang National University (Institute of Agriculture & Life Science)) ;
  • Kang, Ye-Seong (Department of Agro-System Engineering, GyeongSang National University (Institute of Agriculture & Life Science)) ;
  • Jun, Sae-Rom (Department of Agro-System Engineering, GyeongSang National University (Institute of Agriculture & Life Science)) ;
  • Park, Jun-Woo (Department of Agro-System Engineering, GyeongSang National University (Institute of Agriculture & Life Science)) ;
  • Song, Hye-Young (Department of Agro-System Engineering, GyeongSang National University (Institute of Agriculture & Life Science)) ;
  • Kang, Kyeong-Suk (Department of Agro-System Engineering, GyeongSang National University (Institute of Agriculture & Life Science)) ;
  • Kang, Dong-Woo (Department of Plant Bioscience, Pusan National University (Natural Resources & Life Science)) ;
  • Zou, Kunyan (Department of Plant Bioscience, Pusan National University (Natural Resources & Life Science)) ;
  • Jun, Tae-Hwan (Department of Plant Bioscience, Pusan National University (Natural Resources & Life Science))
  • 장시형 (경상대학교 농업생명과학대학 애그로시스템공학전공 (농업생명과학연구원)) ;
  • 유찬석 (경상대학교 농업생명과학대학 애그로시스템공학전공 (농업생명과학연구원)) ;
  • 강예성 (경상대학교 농업생명과학대학 애그로시스템공학전공 (농업생명과학연구원)) ;
  • 전새롬 (경상대학교 농업생명과학대학 애그로시스템공학전공 (농업생명과학연구원)) ;
  • 박준우 (경상대학교 농업생명과학대학 애그로시스템공학전공 (농업생명과학연구원)) ;
  • 송혜영 (경상대학교 농업생명과학대학 애그로시스템공학전공 (농업생명과학연구원)) ;
  • 강경석 (경상대학교 농업생명과학대학 애그로시스템공학전공 (농업생명과학연구원)) ;
  • 강동우 (부산대학교 생명자원과학대학 식물생명과학과) ;
  • 쩌우쿤옌 (부산대학교 생명자원과학대학 식물생명과학과) ;
  • 전태환 (부산대학교 생명자원과학대학 식물생명과학과)
  • Received : 2019.10.01
  • Accepted : 2019.12.02
  • Published : 2019.12.30
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Abstract

Soybean is one of the most important crops of which the grains contain high protein content and has been consumed in various forms of food. Soybean plants are generally cultivated on the field and their yield and quality are strongly affected by climate change. Recently, the abnormal climate conditions, including heat wave and heavy rainfall, frequently occurs which would increase the risk of the farm management. The real-time assessment techniques for quality and growth of soybean would reduce the losses of the crop in terms of quantity and quality. The objective of this work was to develop a simple model to estimate the growth of soybean plant using a multispectral sensor mounted on a rotor-wing unmanned aerial vehicle(UAV). The soybean growth model was developed by using simple linear regression analysis with three phenotypic data (fresh weight, dry weight, leaf area index) and two types of vegetation indices (VIs). It was found that the accuracy and precision of LAI model using GNDVI (R2= 0.789, RMSE=0.73 ㎡/㎡, RE=34.91%) was greater than those of the model using NDVI (R2= 0.587, RMSE=1.01 ㎡/㎡, RE=48.98%). The accuracy and precision based on the simple ratio indices were better than those based on the normalized vegetation indices, such as RRVI (R2= 0.760, RMSE=0.78 ㎡/㎡, RE=37.26%) and GRVI (R2= 0.828, RMSE=0.66 ㎡/㎡, RE=31.59%). The outcome of this study could aid the production of soybeans with high and uniform quality when a variable rate fertilization system is introduced to cope with the adverse climate conditions.

콩은 식량작물 중 단백질 함량이 매우 높고 식생활에서 여러가지 형태로 소비되기 때문에 매우 중요한 식량자원 중 하나이다. 콩은 일반적으로 노지에서 재배되기 때문에 콩의 생산량 및 품질은 갑작스런 기후 변화에 큰 영향을 받는다. 최근 폭염 및 폭우 등과 같은 이상기후로 인해 콩의 생산량이 불안정해짐에 따라 콩의 생육을 실시간으로 추정하여 품질저하를 예방할 수 있는 기술 개발이 필요하다. 본 연구에서는 회전익무인기에 장착된 다중분광 센서를 이용하여 콩 생육을 추정하기 위해 수행되었다. 반사값을 이용하여 산출된 정규화 식생지수(NDVI, GNDVI)와 단순비 식생지수(RRVI, GRVI)와 콩 생육 데이터(생체중, 건물중, 엽면적지수)로 선형회귀분석을 실시하여 생육 추정 모델을 개발하였다. 그 결과, 정규화 식생지수인 NDVI를 이용한 엽면적 지수 추정 모델(R2=0.587, RMSE=1.01 ㎡/㎡, RE=48.98%)보다 GNDVI를 이용한 엽면적 지수 추정 모델(R2=0.789, RMSE=0.73 ㎡/㎡, RE=34.91%)이 높은 정밀도가 나타났으며, 단순비 식생지수를 이용한 엽면적 지수 추정 모델 RRVI (R2=0.760, RMSE=0.78 ㎡/㎡, RE=37.26%) GRVI (R2=0.828, RMSE=0.66 ㎡/㎡, RE=31.59%)과 비교 했을 때, 단순비 식생지수에서 높은 정밀도가 나타났다. 기후변화에 대체하기 위해 재식밀도 및 변량 시비와 같은 재배관리법이 적용된다면, 고품질의 콩을 생산하는데 도움이 될 것으로 판단된다.

Keywords

References

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