Journal of Bio-Environment Control (생물환경조절학회지)

The Korean Society for Bio-Environment Control (한국생물환경조절학회)
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Development of Kimchi Cabbage Growth Prediction Models Based on Image and Temperature Data

영상 및 기온 데이터 기반 배추 생육예측 모형 개발

  • Min-Seo Kang (Division of Vegetable, National Institute of Horticultural and Herbal Science, RDA) ;
  • Jae-Sang Shim (Division of Vegetable, National Institute of Horticultural and Herbal Science, RDA) ;
  • Hye-Jin Lee (Division of Vegetable, National Institute of Horticultural and Herbal Science, RDA) ;
  • Hee-Ju Lee (Division of Vegetable, National Institute of Horticultural and Herbal Science, RDA) ;
  • Yoon-Ah Jang (Division of Vegetable, National Institute of Horticultural and Herbal Science, RDA) ;
  • Woo-Moon Lee (Division of Vegetable, National Institute of Horticultural and Herbal Science, RDA) ;
  • Sang-Gyu Lee (Division of Vegetable, National Institute of Horticultural and Herbal Science, RDA) ;
  • Seung-Hwan Wi (Division of Vegetable, National Institute of Horticultural and Herbal Science, RDA)
  • 강민서 (국립원예특작과학원 채소과) ;
  • 심재상 (국립원예특작과학원 채소과) ;
  • 이혜진 (국립원예특작과학원 채소과) ;
  • 이희주 (국립원예특작과학원 채소과) ;
  • 장윤아 (국립원예특작과학원 채소과) ;
  • 이우문 (국립원예특작과학원 채소과) ;
  • 이상규 (국립원예특작과학원 채소과) ;
  • 위승환 (국립원예특작과학원 채소과)
  • Received : 2023.10.10
  • Accepted : 2023.10.24
  • Published : 2023.10.31
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Abstract

This study was conducted to develop a model for predicting the growth of kimchi cabbage using image data and environmental data. Kimchi cabbages of the 'Cheongmyeong Gaual' variety were planted three times on July 11th, July 19th, and July 27th at a test field located at Pyeongchang-gun, Gangwon-do (37°37' N 128°32' E, 510 elevation), and data on growth, images, and environmental conditions were collected until September 12th. To select key factors for the kimchi cabbage growth prediction model, a correlation analysis was conducted using the collected growth data and meteorological data. The correlation coefficient between fresh weight and growth degree days (GDD) and between fresh weight and integrated solar radiation showed a high correlation coefficient of 0.88. Additionally, fresh weight had significant correlations with height and leaf area of kimchi cabbages, with correlation coefficients of 0.78 and 0.79, respectively. Canopy coverage was selected from the image data and GDD was selected from the environmental data based on references from previous researches. A prediction model for kimchi cabbage of biomass, leaf count, and leaf area was developed by combining GDD, canopy coverage and growth data. Single-factor models, including quadratic, sigmoid, and logistic models, were created and the sigmoid prediction model showed the best explanatory power according to the evaluation results. Developing a multi-factor growth prediction model by combining GDD and canopy coverage resulted in improved determination coefficients of 0.9, 0.95, and 0.89 for biomass, leaf count, and leaf area, respectively, compared to single-factor prediction models. To validate the developed model, validation was conducted and the determination coefficient between measured and predicted fresh weight was 0.91, with an RMSE of 134.2 g, indicating high prediction accuracy. In the past, kimchi cabbage growth prediction was often based on meteorological or image data, which resulted in low predictive accuracy due to the inability to reflect on-site conditions or the heading up of kimchi cabbage. Combining these two prediction methods is expected to enhance the accuracy of crop yield predictions by compensating for the weaknesses of each observation method.

본 연구는 영상데이터와 환경데이터를 활용하여 배추의 생육을 예측할 수 있는 모형을 개발하기 위하여 수행되었다. 강원도 평창군에 소재한 시험포에 '청명가을' 배추를 7월 11일, 7월 19일, 7월 27일 3차례 정식하여 9월 12일까지 생육, 영상, 환경데이터를 수집하였다. 배추 생육예측 모형에 활용할 핵심인자를 선발하기 위하여 수집한 생육데이터와 기상데이터를 활용해 요소간 상관분석을 수행한 결과 생체중과 GDD, 생체중과 누적일사량의 상관계수가 0.88로 높은 상관계수를 보였으며, 생체중과 초장, 생체중과 피복면적이 각각0.78, 0.79로 유의미한 상관 관계를 보였다. 높은 상관관계를 보인 요소들 중에서 선행문헌을 참고하여 모형개발에 활용할 핵심요소로 영상에서는 피복면적을 환경데이터에서는 생육도일(GDD)을 선정하였다. GDD, 피복면적, 생육데이터를 조합하여 배추의 생체중, 엽수, 엽면적 예측 모형을 개발하였다. 단 요인 모형으로 2차함수, 시그모이드, 로지스틱 모형을 제작하였으며 평가 결과 시그모이드 형태의 예측 모형이 가장 설명력이 좋았다. GDD와 피복면적을 조합한 다요인 생육예측 모형을 개발한 결과 생체중, 엽수, 엽면적의 결정계수가 0.9, 0.95, 0.89으로 단요인 예측모형보다 예측정확도가 개선된것을 확인할 수 있었다. 개발한 모형을 검증하기 위하여 검증시험포의 조사결과로 검증한 결과 관측 값과 예측 값의 결정계수는 0.91이며 RMSE가 134.2g으로 높은 예측 정확도를 보였다. 기존의 생육 관측의 경우 기상데이터로만 예측을 하거나 영상데이터로만 예측하는 경우가 많았는데 이는 현장의 상태를 반영하지 못하거나 배추가 결구 되는 특성을 반영하지 못해 예측 정확도가 낮았다. 두 예측방법을 혼합해 각 관측방법의 약점을 보완해 줌으로써 대한민국에서 수행되고 있는 기간채소 작황예측의 정확도를 높일 수 있을 것으로 기대된다.

Keywords

Acknowledgement

본 연구는 농림식품기술기획평가원의 노지분야스마트농업기술단기고도화사업(과제번호: 322032-3)의 지원을 받아 수행되었음. 본 연구는 2023년도 농촌진흥청 국립원예특작과학원 전문연구원 과정 지원사업에 의해 이루어진 것임.

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