Browse > Article
http://dx.doi.org/10.7744/cnujas.2010.37.2.303

Study on Quality Factor Measurement for Cherry Tomato using Color Imagery  

Kim, Dae-Yong (Department of Biosystem Machinery Engineering, Chungnam National University)
Oh, Hyun-Keun (Department of Biosystem Machinery Engineering, Chungnam National University)
Lee, Nam-Keun (Department of Biosystem Machinery Engineering, Chungnam National University)
Kim, Young-Sik (Department of Plant Industry Engineering, Sangmyung University)
Cho, Byung-Kwan (Department of Biosystem Machinery Engineering, Chungnam National University)
Publication Information
Korean Journal of Agricultural Science / v.37, no.2, 2010 , pp. 303-308 More about this Journal
Abstract
Surface color is the most important quality factor for the grade evaluation of cherry tomato. Color is one of the representative indicators for the maturity which is closely related to the internal quality of cherry tomato, such as firmness, sugar content, and acidity. This study was carried out to investigate the relationship between surface color and internal quality of cherry tomatoes harvested from both hydroponic and soil culture at different ripening stages. To calculate the color values of cherry tomatoes an automatic color imaging system was constructed. A specially designed image processing algorithm for the color measurement was developed. The color values of L*, a*, b* were calculated from the initial color values of RGB and then compared with the internal quality. Statistical analyses indicated that the internal quality was more highly correlated with the surface color than size of cherry tomatoes. Color image features were also investigated to detect external damage of cherry tomatoes. The value of (R value - R mean value)/R mean value was the most effective image feature for the detection of damaged areas on the surface of cherry tomatoes. The results of this study demonstrated the feasibility of color sorting process as an alternative of the conventional drum type size sorting system for cherry tomato industry.
Keywords
Cherry tomato; Nondestructive sorting; Image processing; Internal quality; Damage detection;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Lana, M.M., L.M.M. Tijskens, O. van Kooten. 2006. Effects of storage temperature and stage of ripening on RGB colour aspects of fresh-cut tomato pericarp using video image analysis. Journal of Food Engineering 77(4): 871-879.   DOI   ScienceOn
2 Lana, M.M., L.M.M. Tijskens, A. de Theije, A. Hogenkamp, O. van Kooten. 2006. Assessment of changes in optical properties of fresh-cut tomato using video image analysis. Postharvest Biology and Technology 41(3): 296-306.   DOI   ScienceOn
3 Laykin, S., V. Alchanatis, E. Fallik, Y. Edan. 2002. Image-processing algorithms for tomato classification. Transactions of the Asae 45(3): 851-858.
4 Pedro, A.M.K., M.M.C. Ferreira. 2007. Simultaneously calibrating solids, sugars and acidity of tomato products using PLS2 and NIR spectroscopy. Analytica Chimica Acta 595(1-2): 221-227.   DOI   ScienceOn
5 Polder, G., G.W.A.M. van der Heijden, I.T. Young. 2003. Tomato sorting using independent component analysis on spectral images. Real-Time Imaging 9(4): 253-259.   DOI   ScienceOn
6 USDA. 1997. United states standards for grades of fresh tomatoes. United States Department of Agriculture, Agricultural Marketing Service, Washington DC.
7 김대용, 조병관. 2009. 착색도를 이용한 방울토마토 품질측정에 관한 연구. 한국농업기계학회 2009 동계 학술대회 논문집 14(1): 382-386.
8 박우포, 조성환, 김철환. 2002. 포장 조건에 따른 방울토마토의 저장 중 품질 특성 변화. 한국식품저장유통학회지 9(2): 121-125.   과학기술학회마을
9 최규홍, 이강진, 최동수, 윤진하. 1999. 토마토 자동 선별 시스템 개발. 한국농업기계학회 1999년 하계학술대회 논문집 4(2): 282-289.
10 Baranska, M., W. Schutz, H. Schulz. 2006. Determination of lycopene and beta-carotene content in tomato fruits and related products: Comparison of FT-Raman, ATRIR, and NIR spectroscopy. Analytical Chemistry 78(24): 8456-8461.   DOI   ScienceOn
11 Batu, A. 2004. Determination of acceptable firmness and colour values of tomatoes. J. Food Engr. 61(3): 471-475.   DOI   ScienceOn
12 Choi, K., G. Lee, Y.J. Han, J.M. Bunn. 1995. Tomato maturity evaluation using color image analysis. Transactions of the ASAE 38(1): 171-176.
13 Clement, A., M. Dorais, M. Vernon. 2008. Nondestructive Measurement of Fresh Tomato Lycopene Content and Other Physicochemical Characteristics Using Visible-NIR Spectroscopy. Journal of Agricultural and Food Chemistry 56(21): 9813-9818.   DOI   ScienceOn
14 Flores, K., M.T. Sanchez, D. Perez-Marin. 2009. Feasibility in NIRS instruments for predicting internal quality in intact tomato. Journal of Food Engineering 91(2): 311- 318.   DOI   ScienceOn
15 Jahns, G., H.M. Nielsen, W. Paul. 2001. Measuring image analysis attributes and modelling fuzzy consumer aspects for tomato quality grading. Computers and Electronics in Agriculture 31(1): 17-29.   DOI   ScienceOn