CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Comparative Analysis of Various Soft Computing Techniques for Classification of Fruits

by Harmandeep Singh, Baljit Singh Khehra
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 178 - Number 7
Year of Publication: 2017
Authors: Harmandeep Singh, Baljit Singh Khehra
10.5120/ijca2017913963

Harmandeep Singh, Baljit Singh Khehra . Comparative Analysis of Various Soft Computing Techniques for Classification of Fruits. International Journal of Computer Applications. 178, 7 ( Nov 2017), 1-5. DOI=10.5120/ijca2017913963

@article{ 10.5120/ijca2017913963,
author = { Harmandeep Singh, Baljit Singh Khehra },
title = { Comparative Analysis of Various Soft Computing Techniques for Classification of Fruits },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2017 },
volume = { 178 },
number = { 7 },
month = { Nov },
year = { 2017 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume178/number7/28690-2017913963/ },
doi = { 10.5120/ijca2017913963 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:49:43.956318+05:30
%A Harmandeep Singh
%A Baljit Singh Khehra
%T Comparative Analysis of Various Soft Computing Techniques for Classification of Fruits
%J International Journal of Computer Applications
%@ 0975-8887
%V 178
%N 7
%P 1-5
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Fruit classification and recognition techniques play a significant role in various fruit based computer vision systems. Many techniques have been proposed so far to classify and recognize fruit images. However, these techniques not provide efficient results whenever fruit images have poor visibility due to poor environmental conditions. The overall objective of this paper is to study and explore various techniques which have been designed so far to classify and recognize fruit images. Comparisons have been drawn between some well-known fruit classification and recognition techniques based upon certain features. It has been observed that soft computing based fruit classification and recognition techniques performs efficiently than existing techniques.

References
  1. Giacomo C. Et.al, (2015). Automatic Classification of Friut Defects based on Co-occurence Matrix and Neural Networks. Proceedings of the Federated Conference on Computer Science and Information systems pp.861-867
  2. Seema et.al, (2015). Automatic Fruits Grading and Classification system using Computer Vision: A Review. Second International Conference on Advances in Computing and Communication Engineering pp.598-603.
  3. VaniAshok et.al, (2014). Automatic Quality Evaluation of Fruits using Probabilistic Neural Network Approach. International Conference on Contemporary Computing and Informatics(IC3I) pp.308-311.
  4. Chandan K. Et.al, (2015).
  5. Shiv R. Et.al,(2012). Detection and classification of Apple Fruit Diseases using Complete Local Binary Patterns. Third International Conference on Computer and Communication Technology pp.346-351.
  6. Suriya J. Et.al, (2012). Multi Class Support Vector Machine Classifier for Machine Vision Application. International Conference on Communications and Signal Processing (ICCSP) pp.0001-0005.
  7. Watcharin K. Et.al, (2007). Fitting a Pineapple Model for Automatic Maturity Grading. IEEE ICIP pp.257-260.
  8. Yasuo S. Et.al, (2003). Eggplant classification using Artificial Neural Network. pp.1013-1018.
  9. AbdulHamid. H et.al, (2012). Image-Based Date Fruit Classification. IV International Congress on Ultra Modern Telecommunications and control systems. pp.357-361
  10. S.Janardhana et.al, (2013) Computer Aided Inspection system For Food Products using Machine Vision: a Review. International conference on current Trends in Engineering and Technology , ICCTET,2013 pp.29-33.
  11. A.M Aibinu et.al, (2011) Automatic Fruits Identification system Using Hybrid Technique Sixth IEEE International Symposium on Electronic Design, Test and Application. pp.217-221.
  12. Fabio A.F. et.al, (2012) Automatic Classifier Fusion for Produce Recognition. XXV SIBGRAPI Conference on Graphics, Patterns and Images. pp.252-259.
  13. Lin, H. et.al, 2008. Machine Recognition for Broad-Leaved Trees Based on Synthetic Features of Leaves Using Probabilistic Neural Network. Proceedings of IEEE International Conference on Computer Science and Software Engineering, 12-14 Dec. Wuhan, Hubei, China, pp.871-887.
  14. Philips, R. D., et al. 2014. An SMP Soft classification algorithm for remote sensing. Computers & Geosciences. vol. 68, pp.73-80.
  15. Liu, C. et.al, 2001. A Shape and Texture Based Enhanced Fisher Classifier for Face Recognition. IEEE Transactions on Image Processing. vol. 10, no. 4, pp.598-608.
  16. Kishitij, F. et.al, 2014. Detection and classification of plant diseases. International Journal of Engineering Research and General Science. vol.2, no.6, pp.868-874.
  17. Owirka, G.J. et.al, Template-Based SAR ATR Performance Using Different Image Enhancement Techniques. MIT Lincoln Laboratory, pp.1-13.
  18. Das, R. et.al, 2012. Image Segmentation Techniques. IJECT vol. 3, no.1, pp.66-70.
  19. Trier, O. et.al, 1996. Feature Extraction Methods for Character Recognition: A survey. Pattern Recognition. Vol.26, no.4, pp.641-662.
  20. Harvey, N.R. et.al, 2002. Comparison of GENIE and Conventional Supervised Classifiers for Multispectral Image Feature Extraction. IEEE Transactions on Geoscience and Remote Sensing. FEBRUARY 2002. vol. 40, no. 2
  21. Khehra, B.S. and Singh. A.P. 2011. Digital Mammogram Segmentation using Non-Shannon Measures of Entropy. Proceedings of the World Congress on Engineering. WCE 2011, London, U.K. Vol.2
  22. Anke, M.B. 2004. Pattern recognition for medical imaging. Elsevier Academic Press, UK, pp.14-18.
  23. Li, H. et.al, 2001. Computerized radiographic mass detection-part-II: lesion site selection by morphological enhancement and contextual segmentation. IEEE Transactions on medical imaging. vol.20, no. 4, pp.302-313.
  24. Wei, L. et.al, 2005. A study of several machine- learning methods for classification of malignant and benign clustered microcalcifications. IEEE transactions on Medical Imaging. vol. 24, no.3, pp.371-380,
  25. Cheng, H.D, 2006. Approaches for automated detection and classification of masses in mammograms. Pattern Recognition. vol.39, pp.646-668.
  26. Ross, p. et.al, 2002. Learning a Procedure That Can Solve Hard Bin-Packing Problems: A New GA-Based Approach to Hyper-heuristics. European Journal of Operational Research. vol.137, no.1, pp. 1295–1306.
  27. Kim, M.J. et.al, 2006. An evolutionary approach to the combination of multiple classifiers to predict a stock price index. Expert Systems with Applications. vol.31, no.2, pp.241–247.
  28. Ho, S.Y. et.al, 2004. Design of accurate classifiers with a compact fuzzy-rule base using an evolutionary scatter partition of feature space. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics focuses on cybernetics, including communication and control across humans, machines and organizations at the structural or neural level. vol.34, no.2, pp.1031-1044.
  29. Burke, E.K.et.al, 2010. A Classification of Hyper-heuristic Approaches. Handbook of Meta-heuristics International Series in Operations Research & Management Science. vol.146, pp .449-468.
  30. Villiammal, N. et.al, 2012. Efficient feature fusion, selection & classification technique for plant leaf image retrieval system. Proceedings of ACM 2nd International Conference on Computational Science, Engineering and Information Technology, 26-28 Oct. Coimbatore Tamil Nadu, India, pp.132-137.
  31. Gill,J. et.al, 2014. A Review of Automatic Fruit Classification Using Soft Computing Techniques. IJCSEE (online). Vol.2, no.2, pp.99-105.
  32. Cho, S. B. 1997. Neural Network classifiers for Recognizing Totally Unconstrained Handwritten Numerals. IEEE Tranactioms on Neural Network. vol.8, no.1, pp.209-310.
  33. https://en.wikipedia.org/wiki/Genetic_algorithm
  34. D.M. Mukhopadhyay, M.O. Balitanas, A. Farkhod, S.H. Jeon, and D. Bhattacharyya, Genetic Algorithm: A Tutorial Review, International Journal of of Grid and Distributed Computing, Vol.2, No.3, 2009, pp. 25-32
  35. http://subtlearray.tumblr.com/post/42779370765
  36. https://en.wikipedia.org/wiki/Backpropagation
  37. https://en.wikipedia.org/wiki/Fuzzy_clustering
Index Terms

Computer Science
Information Sciences

Keywords

Fruits classifier Soft computing hybrid technique

Powered by PhDFocusTM