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.

서 론
재료 및 방법
    1. 실험 재료 및 재배 조건
    2. 데이터 수집
    3. 실측 데이터와 영상 데이터 비교 분석
    4. 회귀분석과 인공 신경망을 활용한 모델 선정
    5. 모델 성능 검증
    6. 통계분석
결과 및 고찰
    1. 토마토 엽장·엽폭·엽면적의 관계
    2. 실측 데이터와 영상 데이터 측정값 비교
    3. 엽면적 추정 회귀모델 선정
    4. 모델 성능 검정
적 요
ACKNOWLEDGMENTS
REFERENCES

• 이규원(국립목포대학교 원예과학과) | Gyu Won Lee (Department of Horticultural Science, Mokpo National University, Muan 58554, Korea)
• 구희웅(국립목포대학교 원예과학과) | Hee Woong Goo (Department of Horticultural Science, Mokpo National University, Muan 58554, Korea)
• 송욱진(국립목포대학교 원예과학과) | Wook Jin Song (Department of Horticultural Science, Mokpo National University, Muan 58554, Korea)
• 김현문(국립목포대학교 원예과학과) | Hyeon Moon Kim (Department of Horticultural Science, Mokpo National University, Muan 58554, Korea)
• 조영열(제주대학교 원예학과) | Young Yeol Cho (Major of Horticultural Science, Jeju National University, Jeju 63243, Korea)
• 박경섭(국립목포대학교 원예과학과) | Kyoung Sub Park (Department of Horticultural Science, Mokpo National University, Muan 58554, Korea) Corresponding author