Korean Journal of Agricultural and Forest Meteorology (한국농림기상학회지)

Korean Society of Agricultural and Forest Meteorology (한국농림기상학회)
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Estimation of Fresh Weight and Leaf Area Index of Soybean (Glycine max) Using Multi-year Spectral Data

다년도 분광 데이터를 이용한 콩의 생체중, 엽면적 지수 추정

  • Jang, Si-Hyeong (Department of Bio-system Engineering, GyeongSang National University (Institute of Agriculture & Life Science)) ;
  • Ryu, Chan-Seok (Department of Bio-system Engineering, GyeongSang National University (Institute of Agriculture & Life Science)) ;
  • Kang, Ye-Seong (Department of Bio-system Engineering, GyeongSang National University (Institute of Agriculture & Life Science)) ;
  • Park, Jun-Woo (Department of Bio-system Engineering, GyeongSang National University (Institute of Agriculture & Life Science)) ;
  • Kim, Tae-Yang (Department of Bio-system Engineering, GyeongSang National University (Institute of Agriculture & Life Science)) ;
  • Kang, Kyung-Suk (Department of Bio-system Engineering, GyeongSang National University (Institute of Agriculture & Life Science)) ;
  • Park, Min-Jun (Department of Bio-system Engineering, GyeongSang National University (Institute of Agriculture & Life Science)) ;
  • Baek, Hyun-Chan (Department of Bio-system Engineering, GyeongSang National University (Institute of Agriculture & Life Science)) ;
  • Park, Yu-hyeon (Department of Plant Bioscience, Pusan National University (Natural Resources & 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)) ;
  • Kim, Min-Cheol (Department of Plant Bioscience, Pusan National University (Natural Resources & Life Science)) ;
  • Kwon, Yeon-Ju (Department of Plant Bioscience, Pusan National University (Natural Resources & Life Science)) ;
  • Han, Seung-ah (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 (경상국립대학교 바이오시스템공학과 (농업생명과학연구원)) ;
  • 유찬석 (경상국립대학교 바이오시스템공학과 (농업생명과학연구원)) ;
  • 강예성 (경상국립대학교 바이오시스템공학과 (농업생명과학연구원)) ;
  • 박준우 (경상국립대학교 바이오시스템공학과 (농업생명과학연구원)) ;
  • 김태양 (경상국립대학교 바이오시스템공학과 (농업생명과학연구원)) ;
  • 강경석 (경상국립대학교 바이오시스템공학과 (농업생명과학연구원)) ;
  • 박민준 (경상국립대학교 바이오시스템공학과 (농업생명과학연구원)) ;
  • 백현찬 (경상국립대학교 바이오시스템공학과 (농업생명과학연구원)) ;
  • 박유현 (부산대학교 생명자원과학대학 식물생명과학과) ;
  • 강동우 (부산대학교 생명자원과학대학 식물생명과학과) ;
  • 쩌우쿤옌 (부산대학교 생명자원과학대학 식물생명과학과) ;
  • 김민철 (부산대학교 생명자원과학대학 식물생명과학과) ;
  • 권연주 (부산대학교 생명자원과학대학 식물생명과학과) ;
  • 한승아 (부산대학교 생명자원과학대학 식물생명과학과) ;
  • 전태환 (부산대학교 생명자원과학대학 식물생명과학과)
  • Received : 2021.12.06
  • Accepted : 2021.12.27
  • Published : 2021.12.30
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Abstract

Soybeans (Glycine max), one of major upland crops, require precise management of environmental conditions, such as temperature, water, and soil, during cultivation since they are sensitive to environmental changes. Application of spectral technologies that measure the physiological state of crops remotely has great potential for improving quality and productivity of the soybean by estimating yields, physiological stresses, and diseases. In this study, we developed and validated a soybean growth prediction model using multispectral imagery. We conducted a linear regression analysis between vegetation indices and soybean growth data (fresh weight and LAI) obtained at Miryang fields. The linear regression model was validated at Goesan fields. It was found that the model based on green ratio vegetation index (GRVI) had the greatest performance in prediction of fresh weight at the calibration stage (R2=0.74, RMSE=246 g/m2, RE=34.2%). In the validation stage, RMSE and RE of the model were 392 g/m2 and 32%, respectively. The errors of the model differed by cropping system, For example, RMSE and RE of model in single crop fields were 315 g/m2 and 26%, respectively. On the other hand, the model had greater values of RMSE (381 g/m2) and RE (31%) in double crop fields. As a result of developing models for predicting a fresh weight into two years (2018+2020) with similar accumulated temperature (AT) in three years and a single year (2019) that was different from that AT, the prediction performance of a single year model was better than a two years model. Consequently, compared with those models divided by AT and a three years model, RMSE of a single crop fields were improved by about 29.1%. However, those of double crop fields decreased by about 19.6%. When environmental factors are used along with, spectral data, the reliability of soybean growth prediction can be achieved various environmental conditions.

콩은 논 대표적인 밭작물로써 온도, 수분, 토양과 같은 환경 조건에 민감하기 때문에 재배 시 포장 관리가 매우 중요하다. 작물 상태를 비파괴적, 비접촉적 방법으로 측정할 수 있는 분광 기술을 활용한다면 작황 예측, 작물 스트레스 및 병충해 판별 등 생육 진단 및 처방을 통해 품질과 수확량을 높일 수 있다. 본 연구에서는 회전익 무인기에 탑재된 다중분광 센서를 이용하여 시험 포장에서 콩 생육 추정 모델 개발하고 재현성을 확인하기 위해 농가 포장에 검증을 수행하였다. 분광 데이터로 산출된 정규화 식생지수(NDVI, GNDVI), 단순비 식생지수(RRVI, GRVI)와 콩 생육 데이터(생체중, LAI)를 선형회귀분석을 실시하여 모델을 개발하였으며 괴산에 위치한 농가포장에서 검증을 실시하였다. 그 결과 생체중의 경우 정규화 식생지수를 이용 시 포화되기 때문에 단순비 식생지수 GRVI를 이용한 모델의 성능이 가장 높았다(R2=0.74, RMSE=246 g/m2, RE=34.2%). 괴산 농가 포장에 생체중 모델 검증 결과 RMSE=392 g/m2, RE=32%로 나타났으며 작부 체계별 나누어 검증 결과 단작 포장과 이모작 포장 생체중 모델은 RMSE=315 g/m2, RE=26% 및 RMSE=381 g/m2, RE=31%로 나타났다. 작부 체계별 포장과 적산온도가 유사한 연도별 시험 포장(2018+2020년, 2019년)을 나누어 생체중 모델 개발한 결과 단년도(2019년)의 성능이 높게 나타났다. 작부 체계별 적산온도가 유사한 검증과 기존 검증 간 비교 결과 단작 포장은 RMSE 및 RE를 기준으로 각각 29.1%와 34.3%로 개선되었으나 이모작 포장은 -19.6%, -31.3%로 저하되었다. 적산온도 이외의 환경 요인, 분광 및 생육 데이터 추가 시 다양한 환경 조건에서 재배되는 콩 생육을 추정 가능할 것으로 판단된다.

Keywords

Acknowledgement

본 성과물은 농촌진흥청 연구사업(세부과제번호: PJ013837022021) 의 지원에 의해 수행되었음.

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