AUTOMATIC CLASSIFICATION USING IMAGE PROCESSING TECHNIQUE IN MATLAB FOR ORANGE FRUITS

Mayhoub, M. A. M.

doi:10.21608/mjae.2020.52363.1017

AUTOMATIC CLASSIFICATION USING IMAGE PROCESSING TECHNIQUE IN MATLAB FOR ORANGE FRUITS

Document Type : Original Article

Author

M. A. M. Mayhoub

Assist. Prof., Ag. Eng. Dept., Fac. of Ag., Ain Shams U., Cairo, Egypt.

10.21608/mjae.2020.52363.1017

Abstract

This research develops an automatic algorithm for orange fruits sorting image processing technique in MATLAB (7.8) program. The orange fruits were acquired using a digital camera in illumination chamber. A picture handling system was produced to quantify the volume and size of orange fruits. Surface ‎images of every orange, caught with an advanced camera, were used in the picture preparing ‎method. An effective calculation was structured and actualized in MATLAB (7.8) programming. ‎The volumes figured demonstrated great concurrence with the real volumes dictated by ‎water displacement technique. The coefficient of determination "R2" of orange was more than 98%, and the size code by the MATLAB Graphical User Interface (GUI) for orange fruits was concurrence and fast compared to the manual method for sizing.‎ Image processing strategy palatably evaluated orange volume and size. In like manner, image ‎processing gives a precise, straightforward, fast, and noninvasive technique to evaluate orange fruits ‎volume and size and can be effortlessly executed in arranging of orange fruits amid postharvest processing.

Keywords

Main Subjects

Processing Engineering of Agricultural Products

References

Abhayawick, L.; J. C. Laguerre; V. Tauzin and A. Duquenoy (2002). Physical properties of Three onion varieties as affected by the moisture content. J. Food Eng. 55(3), 253–262.

Akar, R. and C. Aydin (2005). Some physical properties of gumbo fruit varieties. J. Food Eng. 66, 387–393.

Arakeri, M.P. and Lakshmana (2016). Computer vision-based fruit grading system for quality evaluation of tomato in agriculture industry. Procedia Comput. Sci. 79, 426–433.

Aydin, C. and M. Ozcan (2002). some physico-mechanic properties of terebinth (PictaciaterebinthusL.) Fruits. J. Food Eng. 53, 97–101.

Aydin, C. (2003). Physical properties of almond nut and kernel. J. Food Eng. 60, 315–320.

Aydine, C. and M. Ozcan (2007). Determination of nutritional and physical properties of Myrtle (MyrtuscommunisL.) fruit growing wild in Turkey. J. Food Eng. 79, 453–458.

Codex Alimentarius (2005). Standard for oranges: Codex Stan 245-2004. 1º Amendment. Rome: FAO e WHO, 6 p.

Eissa, A. A.; A. A. Khalik; and A. A. Abdel (2012). Understanding color image processing by machine vision for biological materials. Structure and Function of Food Engineering, pp. 227-274.

European Commission (2011b).European Commission Regulation (EU) No 543/2011 of 7 June 2011 laying down detailed rules for the application of Council Regulation (EC) No 1234/2007 in respect of the fruit and vegetables and processed fruit and vegetables sectors Regulation No 1121/2008 of 13 November 2008 establishing the standard import values for determining the entry price of certain fruit and vegetables.

Fellegari, R. and H. Navid (2011) Determining the orange volume using image processing. International Conference on Food Engineering and Biotechnology IPCBEE vol.9IACSIT Press, Singapore, pp. 180-184.

Ge, P.; Q. WU and Y. SUN (2007). The Design of Fruit Automated Sorting System. In International Conference on Computer and Computing Technologies in Agriculture (pp. 165-170). Springer, Boston, MA.

Hassan, H.E. (2002). study of sortingand grading operations of Egyptian mature oranges using visible laser. Ph.D. Thesis, National Institute of laser enhanced science Cairo Univ., Egypt.

Kang, S.P.; A. R. East and F. J. Trujillo (2008).Colour Vision System Evaluation of Bicolour Fruit: A case study with ‘B74’ mango. Postharvest Biology and Technology 49. pp, 77– 85.

Khoshnam, F.; A. Tabatabaeefar; M. Ghasemi-Varnamkhasti and A. M. Borghei (2007). Mass modeling of pomegranate (PunicagranatumL) fruit with some physical Characteristics. Sci. Hortic. pp. 114, 21–26.

MATLAB (2009). Image processing toolbox help.

Mohsenin, N. N. (1986). Physical Properties of Plant and Animal Materials. Gordon and Breach Science Publishers, New York, NY., pp.94 - 100.

Moreda, G.P.; J. Ortiz-Cañavate; F. J. García-Ramos and M. Ruiz-Altisent (2009). Nondestructive technologies for fruit and vegetable size determination –A review. J.Food Eng. 92 (2), 119–136.

Naik, S.; and B., Patel (2017). Machine vision-based fruit classification and grading-a review. International Journal of Computer Applications, 170(9), 22-34.

Omobuwajo, T.O.; E. A. Akande and L. A. Sanni (1999). Selected physical, mechanical, and Aerodynamic properties of African breadfruit (Treaulia Africana) seeds. J. Food Eng. 40, 241–244.

Papadakis, S.; S. Abdul-Malek; R. E. Kamdem and K. L. Jam (2000). A versatile and Inexpensive technique for measuring color of foods. Food Technol.-Chicago 54, 48–51.

Razmjooy, N.; B.S. Mousavi; F. Soleymani (2012). A realtime mathematical computer method for potato inspection using machine vision. Computer. Math. Appl. 63 (1), 268-279.

Sadegaonkar, V.D. and K.H. Wagh (2015). Automatic Sorting Using Computer Vision & Image Processing for Improving Apple Quality. International Journal of Innovative Research and Development, 4(1), pp. 543-546.

Sessiz, A.; R. Esgici and S. Kizil (2007). Moisture-dependent physical properties of caper (Capparisssp.) Fruit. J. Food Eng. 79, 1426–1431.

Zhou, L.; V. Chalana and Y. Kim(1998). A PC-based machine vision system for real-time computeraided potato inspection. Int. J. Imaging Syst. Technol. 9(6), 423-433.