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.

작물 생육 진단에 있어서 군락 엽면적과 군락 피복은 주한 요소 이다. 최근에는 이러한 측정을 디지털 카메라를 활용하여 RGB 식생지수로 작물 생육을 측정하고 있다. 본 연구는 밀 재배 기간 카메라의 노출 값을 다르게 설정하여 RGB 컬러 식생 지수와 군락 엽면적지수 및 군락피복에 대해 평가하였다. 군락 엽면적, 군락 피복 및 디지털 영상 측정은 출수 16일전부터 출수 후 25일까지 하였다. 일출 후 30분 이내에 촬영하였다. 노출 값은 셔터 속도를3 수준(1/60s, 1/340s, 1/640s)으로 하였다. RGB 컬러 식생지수 분석은 파이썬으로 하였다. 실제 군락 엽면적과 군락 피복간에는 정의 상관관계(r2 =0.94)를 보였다. 군락 피복 이미지의 ExG, GLI, NGRDI, ExG-ExR, MExG, TGI, MNGRD 및 MExG-CIVE는 군락 엽면적 지수와 및 군락 피복과는 정의 상관관계를 보였다. 그러나 CIVE와 ExR은 부의 상관관계를 보였다. 본 연구 결과 1/640s 가 1/60s와 1/320s에 비하여 노출이 높게 설정된 것으로 보였다. 또한, 토양과 녹색 영역을 분리하는데 있어서 너무 어린 시기 보다는 출수 전이 가장 잘 분리되었다. 따라서 야외에서의 다양한 광 조건에서는 과도한 노출보다는 광 간섭을 줄이는 기술과 함께 작물의 생육 시기와 기상환경을 고려하여 노출 값이 설정되어야 할 것이다.