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.

서 론
재료 및 방법
    1. 공시작물 및 재배환경측정
    2. 영상데이터 취득 및 분석
    3. 배추의 생육 및 생리 특성 조사
    4. 통계처리 및 모델 개발
결과 및 고찰
    1. 재배환경에 따른 배추의 생육 분석
    2. 영상 및 GDD 기반 생육예측 모형 개발
적 요
사 사

• 강민서(국립원예특작과학원 채소과 석사후연구원) | Min-Seo Kang (Master Degree Researcher, Division of Vegetable, National Institute of Horticultural and Herbal Science, RDA, Wanju 55365, Korea)
• 심재상(국립원예특작과학원 채소과 연구원) | Jae-Sang Shim (Assistant Researcher, Division of Vegetable, National Institute of Horticultural and Herbal Science, RDA, Wanju 55365, Korea)
• 이혜진(국립원예특작과학원 채소과 농업연구사) | Hye-Jin Lee (Research Officer, Division of Vegetable, National Institute of Horticultural and Herbal Science, RDA, Wanju 55365, Korea)
• 이희주(국립원예특작과학원 채소과 농업연구관) | Hee-Ju Lee (Senior Research Officer, Division of Vegetable, National Institute of Horticultural and Herbal Science, RDA, Wanju 55365, Korea)
• 장윤아(국립원예특작과학원 채소과 농업연구관) | Yoon-Ah Jang (Senior Research Officer, Division of Vegetable, National Institute of Horticultural and Herbal Science, RDA, Wanju 55365, Korea)
• 이우문(국립원예특작과학원 채소과 농업연구관) | Woo-Moon Lee (Senior Research Officer, Division of Vegetable, National Institute of Horticultural and Herbal Science, RDA, Wanju 55365, Korea)
• 이상규(국립원예특작과학원 채소과 농업연구관) | Sang-Gyu Lee (Senior Research Officer, Division of Vegetable, National Institute of Horticultural and Herbal Science, RDA, Wanju 55365, Korea)
• 위승환(국립원예특작과학원 채소과 농업연구사) | Seung-Hwan Wi (Research Officer, Division of Vegetable, National Institute of Horticultural and Herbal Science, RDA, Wanju 55365, Korea) Corresponding author