Computer vision system in real-time for color determination on flat surface food
DOI:
https://doi.org/10.17268/sci.agropecu.2013.01.06Palabras clave:
Computer Vision, RGB model, CIELab model, food quality control, MatlabResumen
Artificial vision systems also known as computer vision are potent quality inspection tools, which can be applied in pattern recognition for fruits and vegetables analysis. The aim of this research was to design, implement and calibrate a new computer vision system (CVS) in real-time for the color measurement on flat surface food. For this purpose was designed and implemented a device capable of performing this task (software and hardware), which consisted of two phases: a) image acquisition and b) image processing and analysis. Both the algorithm and the graphical interface (GUI) were developed in Matlab. The CVS calibration was performed using a conventional colorimeter (Model CIEL* a* b*), where were estimated the errors of the color parameters: eL* = 5.001%, and ea* = 2.287%, and eb* = 4.314 % which ensure adequate and efficient automation application in industrial processes in the quality control in the food industry sector.
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Abdullah, M.Z.; Mohamad-Saleh, J.; Fathinul-Syahir, A. S.; Mohd-Azemi, B.M.N. 2006. Discrimination and classification of fresh-cut starfruits (Averrhoa carambola L.) using automated machine vision system. Journal of Food Engineering 76: 506-523.
Arce-Lopera, C.; Masuda, T.; Kimura, A.; Wada, Y.; Okajima, K. 2013. Luminance distribution as a determinant for visual freshness perception: Evidence from image analysis of a cabbage leaf. Food Quality and Preference 27: 202-207.
Ariana, D.; Guyer, D.E.; Shrestha, B. 2006. Integrating multispectral reflectance and fluorescence imaging for defect detection on apples. Computers and Electronics in Agriculture 50: 148-161.
Blasco, J.; Aleixos, N.; Gómez-Sanchís, J.; Moltó, E. 2009. Recognition and classification of external skin damage in citrus fruits using multispectral data and morphological features. Biosystems Engineering 103: 137-145.
Blasco, J.; Aleixos, N.; Gómez, J., Moltó, E. 2007a. Citrus sorting by identification of the most common defects using multispectral computer vision. Journal of Food Engineering 83: 384-393.
Blasco, J.; Aleixos, N.; Moltó, E. 2007b. Computer vision detection of peel defects in citrus by means of a region oriented segmentation algorithm. Journal of Food Engineering 81: 535-543.
Brosnan, T.; Sun, D.W. 2004. Improving quality inspection of food products by computer vision––a review. Journal of Food Engineering 61: 3-16.
Du, C.J.; Sun, D.W. 2004. Recent developments in the applications of image processing techniques for food quality evaluation. Trends in Food Science & Technology 15: 230-249.
Fathi, M.; Mohebbi, M.; Razavi, S. 2011. Application of Image Analysis and Artificial Neural Network to Predict Mass Transfer Kinetics and Color Changes of Osmotically Dehydrated Kiwifruit. Food and Bioprocess Technology 4: 1357-1366.
Fernández, L.; Castillero, C.; Aguilera, J.M. 2005. An application of image analysis to dehydration of apple discs. Journal of Food Engineering 67: 185-193.
Hunter Labs. 1996. Hunter Lab Color Scale. Insight on Color 8 9. Reston, VA, USA: Hunter Associates Laboratories.
Hunt, R.W.G. 2004. The Reproduction of Colour (6th ed.). Chichester UK: Wiley–IS&T Series in Imaging Science and Technology. ISBN 0-470-02425-9.
Jimenez-Cuesta, M.C.J.; Martinez-Javega, J.M. 1981. Determination of a color index for citrus fruit degreening. Proceedings of the International Society of Citriculture 2: 750-753.
Lefcout, A.M.; Kim, M.S.; Chen, Y.R.; Kang, S. 2006. Systematic approach for using hyperspectral imaging data to develop multispectral imagining systems: Detection of feces on apples. Computers and Electronics in Agriculture 54: 22-35.
León, K.; Mery, D.; Pedreschi, F.; León, J. 2006. Color measurement in L∗a∗b∗ units from RGB digital images. Food Research International 39: 1084-1091.
López-García, F.; Andreu-García, G.; Blasco, J.; Aleixos, N.; Valiente, J.M. 2010. Automatic detection of skin defects in citrus fruits using a multivariate image analysis approach. Computers and Electronics in Agriculture 71: 189-197.
Lunadei, L.; Diezma, B.; Lleó, L.; Ruiz-Garcia, L.; Cantalapiedra, S.; Ruiz-Altisent, M. 2012. Monitoring of fresh-cut spinach leaves through a multispectral vision system. Postharvest Biology and Technology 63: 74-84.
Mendoza, F.; Dejmek, P.; Aguilera, J.M. 2006. Calibrated color measurements of agricultural foods using image analysis. Postharvest Biology and Technology 41: 285-295.
Pedreschi, F.; Mery, D.; Bunger, A.; Yañez, V. 2011. Computer vision classification of potato chips by color. Journal of Food Process Engineering 34: 1714-1728.
Pedreschi, F.; León, J.; Mery, D.; Moyano, P. 2006. Development of a computer vision system to measure the color of potato chips. Food Research International 39: 1092-1098.
Poynton, C.E. 1996. A Technical Introduction to Digital Video. John Wiley & Sons Inc. New York, NY, USA. pp 352.
Pulido, H.G.; de la Vara Salazar, R.; González, P.G.; Martínez, C.T.; Pérez, M.C.T. 2004. Análisis y diseño de experimentos: McGraw-Hill. México. pp 571.
Quevedo, R.; Díaz, O.; Ronceros, B.; Pedreschi, F.; Aguilera, J.M. 2009. Description of the kinetic enzymatic browning in banana (Musa cavendish) slices using non-uniform color information from digital images. Food Research International 42: 1309-1314.
Segnini, S.; Dejmek, P.; Öste, R. 1999. A Low Cost Video Technique for Colour Measurement of Potato Chips. LWT - Food Science and Technology 32: 216-222.
Unay, D.; Gosselin, B. 2007. Stem and calyx recognition on ‘Jonagold’ apples by pattern recognition. Journal of Food Engineering 78: 597-605.
Xiaobo, Z.; Jiewen, Z.; Yanxiao, L. 2007. Apple color grading based on organization feature parameters. Pattern Recognition Letters 28: 2046-2053.
Xing, J.; Jancsók, P.; De Baerdemaeker, J. 2007. Stem-end/Calyx Identification on Apples using Contour Analysis in Multispectral Images. Biosystems Engineering 96: 231-237.
Xul, Q.Z.X; Zhao, J. 2009. On-Line Detection of Defects on Fruit by Machinevision Systems Based on Three-Color-Cameras Systems. Computer and Computing Technologies in Agriculture 2: 2231-2238.
Yam, K.L.; Papadakis, S.E. 2004. A simple digital imaging method for measuring and analyzing color of food surfaces. Journal of Food Engineering 61: 137-142.
Received: 12/12/12
Accepted: 21/03/13
Corresponding author: E-mail: rsiche@unitru.edu.pe (R. Siche)
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