본 연구의 목적은 컨테이너 육묘 시스템을 활용한 참외 접목 묘의 안정적인 생산 가능성을 평가하는 것이었다. 이를 위해, 컨테이너 육묘 시스템과 고온 조건의 플라스틱 온실에서 육묘 한 접수와 대목, 접목묘의 생육을 비교 분석하였다. 접목활착 후 육묘 환경에 따른 참외 접목묘의 생육과 묘소질을 0일, 4일, 7일, 11일, 14일째에 비교하였다. 컨테이너 육묘 시스템에서 는 주야간 온도를 25/20°C, 상대습도를 70%로 설정하여 재 배기간 동안 안정적으로 유지하였으며, 플라스틱 온실 내의 주야간 평균온도는 28.1/15.4°C로 주야간 온도차(DIF)가 더 크게 나타났다. 조사기간 동안 참외 접목묘의 초장은 플라스 틱 온실 육묘 처리구에서 컨테이너 육묘 시스템 처리구보다 더 길게 나타났다. 참외 접목묘 조직의 충실도는 지상부 건물 중을 초장으로 나누어 계산하였다. 육묘장에서 접목한 묘는 접목 후 7－10일 경과하여 활착이 완료되고 초장이 10cm 내 외일 때 출하하여 정식에 이용되게 된다. 본 연구에서 접목활 착 후 7일째에 컨테이너 육묘 시스템에서 재배된 묘의 충실도 는 44.9±2.64mg/cm으로 나타났으며, 플라스틱 온실 육묘 처 리구에서는 24.4±1.56mg/cm로 나타났다. SPAD 평균은 플 라스틱 온실 육묘에서 30.5, 컨테이너 육묘 시스템에서 41.1 로 측정되었다. 이러한 결과는 컨테이너 육묘 시스템의 활용 이 고온기 또는 저일조 시기와 같은 육묘 환경에서도 고품질 모종을 안정적으로 생산할 수 있는 것을 확인하였고, 인공광 을 이용한 육묘 시스템의 활용 범위가 앞으로 더 확대될 것으 로 기대된다.

Tomato is one of the major widely cultivated crops around the world. The leaf area is directly related to the total amount of photosynthesis, which affects the yield and quality of the fruit. Traditional methods of measuring the leaf area are time-consuming and can cause damage to the leaves. To address these problems, various studies are being conducted for measuring the leaf area. In this study, we introduced a model to estimate the leaf area using images of tomatoes. Using images captured by a camera, we measured the leaf length and width and used linear regression analysis to derive the leaf area estimation formula. Furthermore, we used a Neural Network (NN) for additional analysis to compare the accuracy of the models. Initially, to verify the reliability of the image data, we conducted a correlation analysis between the actual measurement data and the image data, which showed a high positive correlation. The leaf area estimation model presented 23 estimation formulas. We used regression analysis to estimate the coefficients of each model and also used employed an artificial neural network analysis to derive high R-squared (R2) values and low Root Mean Square Error (RMSE) values. Among the estimation formulas, the ninth model showed the highest reliability with an R-squared value of 0.863. We conducted a verification experiment to confirm the accuracy of the selected model, and the R-squared value was 0.925. This study confirmed the reliability of data measured from images and the reliability of the leaf area estimation model using image data. These methods are expected to be an important tool in agriculture, using imaging equipment for measuring and monitoring the crop growth.

Tomatoes in greenhouse are a widely cultivated horticultural crop worldwide, accounting for high production and production value. When greenhouse ventilation is minimized during low temperature periods, CO2 enrichment is often used to increase tomato photosynthetic rate and yield. Plant-induced electrical signal (PIES) can be used as a technology to monitor changes in the biological response of crops due to environmental changes by using the principle of measuring the resistance value, or impedance, within the crop. This study was conducted to investigate the relationship between tomato growth data, vital response, and PIES resulting from CO2 enrichment in greenhouse tomatoes. The growth of tomato treated with CO2 enrichment in the morning was significantly better in all items except stem diameter compared to the control, and PIES values were also higher. The growth of tomato continuously applied with CO2 was better in the treatment groups than control, and there was no significant difference in chlorophyll fluorescence and photosynthesis. However, PIES and SPAD values were higher in the CO2 treatment group than control. CO2 enrichment have a direct relationship with PIES, growth increased, and transpiration increased due to the increased leaf area, resulting in increased water absorption, which appears to be reflected in PIES, which measures vascular impedance. Through this, this study suggests that PIES can be used to monitor crops due to environmental changes, and that PIES is a useful method for non-destructively and continuously monitoring changes of crops.