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Computer Vision and Image Analysis based Techniques for Automatic Characterization of Fruits – A Review

by Jyoti A Kodagali, S Balaji
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 50 - Number 6
Year of Publication: 2012
Authors: Jyoti A Kodagali, S Balaji
10.5120/7773-0856

Jyoti A Kodagali, S Balaji . Computer Vision and Image Analysis based Techniques for Automatic Characterization of Fruits – A Review. International Journal of Computer Applications. 50, 6 ( July 2012), 6-12. DOI=10.5120/7773-0856

@article{ 10.5120/7773-0856,
author = { Jyoti A Kodagali, S Balaji },
title = { Computer Vision and Image Analysis based Techniques for Automatic Characterization of Fruits – A Review },
journal = { International Journal of Computer Applications },
issue_date = { July 2012 },
volume = { 50 },
number = { 6 },
month = { July },
year = { 2012 },
issn = { 0975-8887 },
pages = { 6-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume50/number6/7773-0856/ },
doi = { 10.5120/7773-0856 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:47:35.532037+05:30
%A Jyoti A Kodagali
%A S Balaji
%T Computer Vision and Image Analysis based Techniques for Automatic Characterization of Fruits – A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 50
%N 6
%P 6-12
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents the recent developments and application of image analysis and computer vision system in an automatic fruit recognition system. It is essential to throw light on basic concepts and technologies associated with computer vision system, a tool used in image analysis of fruit characterization. In India, in view of the ever-increasing population, losses in handling and processing and the increased expectation of food products of high quality and safety standards, there is a need for the growth of accurate, fast and objective quality determination of fruits. A number of challenges had to be overcome to enable the system to perform automatic recognition of the kind of fruit or fruit variety using the images from the camera. Several types of fruits are subject to significant variation in color and texture, depending on how ripe they are. There are many processes in agriculture where decisions are made based on the appearance of the product. Applications for grading the fruit by its quality, size or ripeness are based on its appearance, as well as a decision on whether it is healthy or diseased. The objective of this paper is to provide in depth introduction of machine vision system, its components and recent work reported on an automatic fruit characterization system.

References
  1. Krishna Kumar Patel & A. Kar & S. N. Jha & M. A. Khan, February 2011, Machine vision system: a tool for quality inspection of food and agricultural products, J Food Sci Technol, DOI 10. 1007/s13197-011-0321-4.
  2. Baxes GA, 1994, Digital image processing principles and applications, Wiley, New York.
  3. Raji a. O and A. O. Alamutu, February 2005, Prospects of Computer Vision Automated Sorting Systems in Agricultural Process operations in Nigeria, CIGR, Journal of Scientific Research and Development". Vol. VII.
  4. Singh M, Kaur G, Kaur S, 7 Oct 2004, Machine vision: an upcoming realm Everyman's science, 186-187.
  5. Gunasekaran, S. , Computer vision technology for food quality assurance, 1996, Trends in Food Science and Technology, 7(8), pp. 245-256.
  6. Locht, P. , Thomsen, K. , Mikkelsen, P. , 1997, Full colour image analysis as a tool for quality control and process development in the food industry, Paper No. 973006, ASAE, 2950 Niles Road, St. Joseph, MI 49085-9659, USA.
  7. P. Sudhakara Rao et al, 2009, Colour Analysis of fruits using machine vision system for Automatic Sorting and Grading, J. Instrum. Soc. India 34 (4) 284-291.
  8. V. Leemans et al, 2002, On-line Fruit Grading according to their External Quality using Machine Vision, Biosystems Engineering 83 (4), 397–404, doi:10. 1006/bioe. 2002. 0131,Journal of Automation and Emerging Technologies, Belgium.
  9. P. Sudhakara Rao et al, summer-fall 2002, New Approaches for Size Determination of Apple Fruits for Automatic Sorting and Grading, Iranian Journal of Electrical and computer engineering, vol. 1, no. 2.
  10. H. Mirzaei et al, A New and Robust Apple Evaluation Method using Image Processing, 29-31Aug 2007, First Joint Congress on Fuzzy and Intelligent Systems Ferdowsi University of Mashhad, Iran.
  11. D. Stajnko, J. Rakun and M. Blanke, 2009, Modelling Apple Fruit Yield Using Image Analysis for Fruit Colour, Shape and Texture, Europ. J. Hort. Sci. , 74 (6). S. 260–267, ISSN 1611-4426.
  12. V. Leemans, M. -F. Destain, A real-time grading method of apples based on features extracted from defects.
  13. Devrim Unay et al, 2010, Automatic grading of Bi-colored apples by multispectral machine vision, Journal of Computers and Electronics in Agriculture, elesevier.
  14. M. B. Lak et al, 2010, Apple Fruits Recognition Under Natural Luminance Using Machine Vision, Advance Journal of Food Science and Technology 2(6): 325-327, ISSN: 2042-4876.
  15. Brendon J. Woodford et al, 2010, Fruit Image Analysis using Wavelets.
  16. Ismail Kavdir et al, 2003, Apple Grading Using Fuzzy Logic.
  17. D. Unay, B. Gosselin, 2006, Automatic defect segmentation of Jonagold apples on multi-spectral images: A comparative study, Postharvest Biology and Technology 42 271–279.
  18. Devrim Unay, Bernard Gosselin, Apple Defect Detection and Quality Classification with MLP-Neural Networks.
  19. Yousef Al Ohali, 2011, Computer vision based date fruit grading system: Design and implementation, Journal of King Saud University – Computer and Information Science 23, 29–36.
  20. Dae Gwan Kim et al, September, 2009, Classification of grapefruit peel diseases using color texture feature analysis, International Journal of Agric & Biol Eng,, Vol. 2 No. 3, USA.
  21. A. Esehaghbeygi et al, 2010, Digital image processing for quality ranking of saffron peach, Int. Agrophysics, 24, 115-120.
  22. Zulham Effendi et al, 2009, Development of Jatropha Curcas Color Grading System for Ripeness Evaluation, European Journal of Scientific Research ISSN 1450-216X Vol. 30 No. 4 , pp. 662-669.
  23. Mohd Z. Abdullah et al, 2002, Color Vision System for Ripeness Inspection of Oil Palm Elaeis Guineenszs, Journal of Food Processing Preservation 26 213-235, Malaysia.
  24. Jose Blasco et al, Computer vision developments for the automatic inspection of fresh and processed fruits, Image Analysis for Agricultural Products and Processes, ISSN 0947-7314, Spain.
  25. Hassan Sadrnia et al, Classification and Analysis of Fruit Shapes in Long TypeWatermelon Using Image Processing, International Journal of Agriculture & Biology 1560–8530/2007/09–1–68–70, Iran.
  26. Mahdi Abbasgolipour, Feb 2010, Sorting Raisins by Machine Vision System,Modern Applied Science, Vol. 4, No. 2.
  27. M. Omid, M. Abbasgolipour, A. Keyhani and S. S. Mohtasebi, 2010, Implementation of an Efficient Image Processing Algorithm for Grading Raisins, International Journal of Signal and Image Processing, Vol. 1-Iss. 1.
  28. Yizhong Wang et al, 2009, Study on fruit quality measurement and evaluation based on color Identification, Proc. of SPIE Vol. 7513 75130F-1.
  29. B Sathya Bama et al, 2011, 3D Color Co-occurance texture features as tool to evaluate quality of fruits, Journal of Scientific and Industrial Research,Vol 70, pp. 912-917.
  30. S. Arivazhagan et al, Oct 2010, Fruit Recognition using Color and Texture Features, Journal of Emerging Trends in Computing and Information Sciences, Vol. 1, No. 2.
Index Terms

Computer Science
Information Sciences

Keywords

Image analysis fruit characterization computer vision

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