Rancang Bangun Computer Vision System (CVS) Sebagai Instrumen Pengukuran Warna Buah-Buahan

https://doi.org/10.22146/agritech.29119

Ferlando Jubelito Simanungkalit(1*), Rosnawyta Simanjuntak(2)

(1) Program Studi Teknologi Hasil Pertanian, Fakultas Pertanian, Universitas HKBP Nommensen Medan, Jl. Sutomo No. 4A Medan, Sumatera Utara
(2) Program Studi Teknologi Hasil Pertanian, Fakultas Pertanian, Universitas HKBP Nommensen Medan, Jl. Sutomo No. 4A Medan, Sumatera Utara
(*) Corresponding Author

Abstract


Color had a correlation with physical appearance, nutritional and chemical content as well as sensory properties which determine the quality of agricultural products and foods. Conventional color measurements were performed destructively using laboratory equipment. Therefore, color measurement methods of agricultural products were needed more quickly, accurately and non-destructively. This study aimed to develop a Computer Vision System (CVS) that can be used as a tool to measure the color of fruits. The designed CVS consists of a 60x60x60 cm black mini photo studio; a pair 15 watt LED lighting, sony α6000 digital camera, a set of laptop and an image processing software applications. Image processing software was programmed using VB.Net 2008 programming language. The developed CVS was calibrated using 24 color charts Macbeth Colorchecker (Gretag-Macbeth, USA). The calibration results of 24 color chart of Macbeth Colorchecker was resulted in a MAPE (Mean Absolute Percentage Error) value of component R / Red = 0%; G / Green = 0% and B / Blue = 0,5%; with 99% accuracy rate. In color measurement, the developed CVS had a 95% accuracy rate.

Keywords


Color measurement; computer vision system; CVS; image processing

Full Text:

PDF


References

Bhargava, A., & Bansal, A. (2018). Fruits and vegetables quality evaluation using computer vision : A review. Journal of King Saud University - Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2018.06.002

Blasco, J., Cubero, S., Gómez-Sanchís, J., Mira, P., & Moltó, E. (2009). Development of a machine for the automatic sorting of pomegranate (Punica granatum) arils based on computer vision. Journal of Food Engineering, 90(1), 27–34. https://doi.org/10.1016/j.jfoodeng.2008.05.035

Brosnan, T., & Sun, D. W. (2004). Improving quality inspection of food products by computer vision - A review. Journal of Food Engineering, 61(1 SPEC.), 3–16. https://doi.org/10.1016/S0260-8774(03)00183-3

Costa, C., Antonucci, F., & Menesatti, P. (2011). Shape Analysis of Agricultural Products : A Review of Recent Research Advances and Potential Application to Computer Vision. 673–692. https://doi.org/10.1007/s11947-011-0556-0

Elmasry, G., Cubero, S., Moltó, E., & Blasco, J. (2012). In-line sorting of irregular potatoes by using automated computer-based machine vision system. Journal of Food Engineering, 112(1–2), 60–68. https://doi.org/10.1016/j.jfoodeng.2012.03.027

Elmasry, G., Kamruzzaman, M., Sun, D., Allen, P., Elmasry, G., Kamruzzaman, M., … Allen, P. (2012). Principles and Applications of Hyperspectral Imaging in Quality Evaluation of Agro-Food Products : A Review Principles and Applications of Hyperspectral Imaging in Quality Evaluation of Agro-Food Products : 8398. https://doi.org/10.1080/10408398.2010.543495

Freitas, J., Gomes, S., & Rodrigues, F. (2012). Applications of computer vision techniques in the agriculture and food industry : a review. 989–1000. https://doi.org/10.1007/s00217-012-1844-2

Garrido-novell, C., Pérez-marin, D., Amigo, J. M., Fernández-novales, J., Guerrero, J. E., & Garrido-varo, A. (2012). Grading and color evolution of apples using RGB and hyperspectral imaging vision cameras. 113, 281–288. https://doi.org/10.1016/j.jfoodeng.2012.05.038

Girolami, A., Napolitano, F., Faraone, D., & Braghieri, A. (2013). Measurement of meat color using a computer vision system. MESC, 93(1), 111–118. https://doi.org/10.1016/j.meatsci.2012.08.010

Iqbal, A., Valous, N. A., Mendoza, F., Sun, D. W., & Allen, P. (2010). Classification of pre-sliced pork and Turkey ham qualities based on image colour and textural features and their relationships with consumer responses. Meat Science, 84(3), 455–465. https://doi.org/10.1016/j.meatsci.2009.09.016

Li, J., Rao, X., & Ying, Y. (2011). Detection of common defects on oranges using hyperspectral reflectance imaging. Computers and Electronics in Agriculture, 78(1), 38–48. https://doi.org/10.1016/j.compag.2011.05.010

Lorente, D., Aleixos, N., Gómez-Sanchis, J., Cubero, S., García-Navarrete, O. L., & Blasco, J. (2012). Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment. Food and Bioprocess Technology, 5(4), 1121–1142. https://doi.org/10.1007/s11947-011-0725-1

Masithoh, R. E., Rahardjo, B., Sutiarso, L., & Hardjoko, A. (2011). Pengembangan computer vision system sederhana untuk menentukan kualitas tomat. AgriTECH, 31(2).

Pallottino, F., Menesatti, P., Costa, C., Paglia, G., de Salvador, F. R., & Lolletti, D. (2010). Image analysis techniques for automated hazelnut peeling determination. Food and Bioprocess Technology, 3(1), 155–159. https://doi.org/10.1007/s11947-009-0211-1

Pascale, D. (2006). RGB Coordinates of the Macbeth Color Checker. The BabelColor Company, 1–16. Retrieved from http://www.babelcolor.com/download/RGB Coordinates of the Macbeth Colorchecker.pdf

Patel, K. K., Kar, A., Jha, S. N., & Khan, M. A. (2012). Machine vision system : a tool for quality inspection of food and agricultural products. 49(April), 123–141. https://doi.org/10.1007/s13197-011-0321-4

Pathare, P. B., Opara, U. L., & Al-Said, F. A. J. (2013). Colour Measurement and Analysis in Fresh and Processed Foods: A Review. Food and Bioprocess Technology, 6(1), 36–60. https://doi.org/10.1007/s11947-012-0867-9

Razmjooy, N., Mousavi, B. S., & Soleymani, F. (2012). A real-time mathematical computer method for potato inspection using machine vision. Computers and Mathematics with Applications, 63(1), 268–279. https://doi.org/10.1016/j.camwa.2011.11.019

Russ, J. C. (2004). Image analysis of food microstructure. CRC press.

Teena, M., Manickavasagan, A., Mothershaw, A., Hadi, S. El, & Jayas, D. S. (2013). Potential of Machine Vision Techniques for Detecting Fecal and Microbial Contamination of Food Products : A Review. 1621–1634. https://doi.org/10.1007/s11947-013-1079-7

Vidal, A., Talens, P., Prats-Montalbán, J. M., Cubero, S., Albert, F., & Blasco, J. (2013). In-Line Estimation of the Standard Colour Index of Citrus Fruits Using a Computer Vision System Developed For a Mobile Platform. Food and Bioprocess Technology, 6(12), 3412–3419. https://doi.org/10.1007/s11947-012-1015-2

Wang, Y., Cui, Y., Chen, S., Zhang, P., Huang, H., & Huang, G. Q. (2009). Study on fruit quality measurement and evaluation based on color identification. 2009 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Process Technology, 7513, 75130F. https://doi.org/10.1117/12.839698

Wu, D., & Sun, D. W. (2013). Colour measurements by computer vision for food quality control - A review. Trends in Food Science and Technology, 29(1), 5–20. https://doi.org/10.1016/j.tifs.2012.08.004

Xiaobo, Z., Jiewen, Z., & Yanxiao, L. (2007). Apple color grading based on organization feature parameters. Pattern Recognition Letters, 28(15), 2046–2053. https://doi.org/10.1016/j.patrec.2007.06.001

Zhang, B., Huang, W., Li, J., Zhao, C., Fan, S., Wu, J., & Liu, C. (2014). Principles , developments and applications of computer vision for external quality inspection of fruits and vegetables : A review. FRIN, 62, 326–343. https://doi.org/10.1016/j.foodres.2014.03.012



DOI: https://doi.org/10.22146/agritech.29119

Article Metrics

Abstract views : 6992 | views : 6922

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Copyright (c) 2020 Ferlando Jubelito Simanungkalit, Rosnawyta Simanjuntak

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

agriTECH has been Indexed by:

Emerging Source Citation Index Directory of Open Access Journals CABI.org Dimensions Google Scholar Sinta Garuda

agriTECH (print ISSN 0216-0455; online ISSN 2527-3825) is published by Faculty of Agricultural Technology, Universitas Gadjah Mada in colaboration with Indonesian Association of Food Technologies.

website statisticsView My Stats