Korean Journal of Agricultural and Forest Meteorology (한국농림기상학회지)

Korean Society of Agricultural and Forest Meteorology (한국농림기상학회)
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Development of Prediction Model for the Na Content of Leaves of Spring Potatoes Using Hyperspectral Imagery

초분광 영상을 이용한 봄감자의 잎 Na 함량 예측 모델 개발

  • Park, Jun-Woo (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)) ;
  • Ryu, Chan-Seok (Department of Bio-System Engineering, GyeongSang National University (Institute of Agriculture & Life Science)) ;
  • Jang, Si-Hyeong (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)) ;
  • Kim, Tae-Yang (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, Hyeon-Chan (Department of Bio-System Engineering, GyeongSang National University (Institute of Agriculture & Life Science)) ;
  • Song, Hye-Young (National Institute of Agricultural Sciences, Rural Development Administration) ;
  • Jun, Sae-Rom (Hortizen Co. Ltd.) ;
  • Lee, Su-Hwan (National Institute of Crop Science, Rural Development Administration)
  • 박준우 (경상국립대학교 바이오시스템공학과 (농업생명과학연구원)) ;
  • 강예성 (경상국립대학교 바이오시스템공학과 (농업생명과학연구원)) ;
  • 유찬석 (경상국립대학교 바이오시스템공학과 (농업생명과학연구원)) ;
  • 장시형 (경상국립대학교 바이오시스템공학과 (농업생명과학연구원)) ;
  • 강경석 (경상국립대학교 바이오시스템공학과 (농업생명과학연구원)) ;
  • 김태양 (경상국립대학교 바이오시스템공학과 (농업생명과학연구원)) ;
  • 박민준 (경상국립대학교 바이오시스템공학과 (농업생명과학연구원)) ;
  • 백현찬 (경상국립대학교 바이오시스템공학과 (농업생명과학연구원)) ;
  • 송혜영 (농촌진흥청 국립농업과학원) ;
  • 전새롬 (주식회사 호티젠) ;
  • 이수환 (농촌진흥청 국립식량과학원)
  • Received : 2021.11.29
  • Accepted : 2021.12.27
  • Published : 2021.12.30
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Abstract

In this study, the leaf Na content prediction model for spring potato was established using 400-1000 nm hyperspectral sensor to develop the multispectral sensor for the salinity monitoring in reclaimed land. The irrigation conditions were standard, drought, and salinity (2, 4, 8 dS/m), and the irrigation amount was calculated based on the amount of evaporation. The leaves' Na contents were measured 1st and 2nd weeks after starting irrigation in the vegetative, tuber formative, and tuber growing periods, respectively. The reflectance of the leaves was converted from 5 nm to 10 nm, 25 nm, and 50 nm of FWHM (full width at half maximum) based on the 10 nm wavelength intervals. Using the variance importance in projections of partial least square regression(PLSR-VIP), ten band ratios were selected as the variables to predict salinity damage levels with Na content of spring potato leaves. The MLR(Multiple linear regression) models were estimated by removing the band ratios one by one in the order of the lowest weight among the ten band ratios. The performance of models was compared by not only R2, MAPE but also the number of band ratios, optimal FWHM to develop the compact multispectral sensor. It was an advantage to use 25 nm of FWHM to predict the amount of Na in leaves for spring potatoes during the 1st and 2nd weeks vegetative and tuber formative periods and 2 weeks tuber growing periods. The selected bandpass filters were 15 bands and mainly in red and red-edge regions such as 430/440, 490/500, 500/510, 550/560, 570/580, 590/600, 640/650, 650/660, 670/680, 680/690, 690/700, 700/710, 710/720, 720/730, 730/740 nm.

본 연구에서는 간척지의 염분 모니터링을 위한 다중 분광 센서를 개발하기 위해 400~1000 nm 초분광센서를 사용하여 봄 감자의 잎 Na 함량 예측 모델을 구축하고자 하였다. 관개조건은 표준, 한해, 염해(2, 4, 8 dS/m)로, 관수량은 증발량을 기준으로 산정하였다. 영양생장기, 괴경형성기, 괴경비대기에 각각 관개를 시작한 후 1주와 2주 후에 잎의 Na 함량을 측정하였다. 잎의 반사율은 10nm 파장 간격을 기준으로 5 nm에서 10nm, 25nm, 50nm FWHM (full width at half maximum)으로 변환되었다. PLS-VIP를 사용하여 봄 감자 잎의 Na 함량에 따른 염분 피해 수준을 예측하기 위한 10개의 밴드비가 선택되었다. 선택된 10개의 밴드비 중 가중치가 가장 낮은 순서대로 밴드비를 하나씩 제거하면서 MLR모델을 추정하였다. 모델의 성능은 R2, MAPE 뿐만 아니라 밴드비의 수, 다중 분광센서를 작게 만들기 위한 최적의 FWHM 수로 비교하였다. 1, 2주차의 영양생장기, 괴경형성기와 2주차의 괴경비대기에서 봄 감자의 잎 Na 함량을 예측하기 위해서는 25 nm의 FWHM을 사용하는 것이 유리하였다. 선택된 밴드필터는 430/440, 490/500, 500/510, 550/560, 570/580, 590/600, 640/650, 650/660, 670/680, 680/690, 690/700, 700/710, 710/720, 720/730, 730/740 nm로 Red 및 Red-edge 영역에서 15개 밴드비가 선택되었다.

Keywords

Acknowledgement

본 연구는 농촌진흥청 연구사업(세부과제명: 영상기법 활용 간척지 작물 염해 예측 기술 개발, 세부과제번호: PJ01388404)의 지원에 의해 이루어진 것임.

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