Advertisement

Shape-based Fruit Recognition and Classification

  • Susovan JanaEmail author
  • Ranjan Parekh
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 776)

Abstract

Classification of fruits is traditionally done using manual resources due to which the time and economic involvements increase adversely with number of fruit types and items per class. In recent times computer based automated techniques have been used to alleviate this problem to a certain extent. These techniques utilize image analysis and pattern recognition methodologies to automatically classify fruits based on their visual features like color, texture, and shape. However, challenges of such techniques include the fact that fruit appearances differ due to natural environments, geographical locations, stages of growth, size, orientations and imaging equipments. In this paper, a shape based fruit recognition approach has been proposed which is independent of many of these factors. It involves a pre-processing step to normalize a fruit image with respect to variations in translation, rotation, scaling and utilizes features which do not change due to varying distances, growth stages and surface appearances of fruits. The proposed method has been applied to 210 images of 7 fruit classes. The overall recognition accuracy ranges from 88–95%.

Keywords

Fruit classification Geometrical transformation Morphological operation Convex polygon Naïve bayes classifier 

References

  1. 1.
    Rachmawati, E., Khodra, M.L., Supriana, I.: Histogram based color pattern identification of multiclass fruit using feature selection. In: 5th International Conference on Electrical Engineering and Informatics (ICEEI), pp. 43–48. IEEE (2015)Google Scholar
  2. 2.
    Roomi, S.M.M., Priya, R.J., Bhumesh, S., Monisha, P.: Classification of mangoes by object features and contour modeling. In: International Conference on Machine Vision and Image Processing (MVIP), pp. 165–168. IEEE (2012)Google Scholar
  3. 3.
    Seng, W.C., Mirisaee, S.H.: A new method for fruits recognition system. In: International Conference on Electrical Engineering and Informatics (ICEEI), vol. 1, pp. 130–134. IEEE (2009)Google Scholar
  4. 4.
    Zawbaa, H.M., Hazman, M., Abbass, M., Hassanien, A.E.: Automatic fruit classification using random forest algorithm. In: 14th International Conference on Hybrid Intelligent Systems (HIS), pp. 164–168. IEEE (2014)Google Scholar
  5. 5.
    Naskar, S., Bhattacharya, T.: A fruit recognition technique using multiple features and artificial neural network. Int. J. Comput. Appl. 116(20), 23–28 (2015)Google Scholar
  6. 6.
    Al-falluji, R.A.A.: Color, shape and texture based fruit recognition system. Int. J. Adv. Res. Comput. Eng. Technol. 5(7), 2108–2112 (2016)Google Scholar
  7. 7.
    Kuang, H.L., Chan, L.L.H., Yan, H.: Multi-class fruit detection based on multiple color channels. In: International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR), pp. 1–7. IEEE (2015)Google Scholar
  8. 8.
    Wang, X., Huang, W., Jin, C., Hu, M., Ren, F.: Fruit recognition based on multi-feature and multi-decision. In: 3rd International Conference on Cloud Computing and Intelligence Systems (CCIS), pp. 113–117. IEEE (2014)Google Scholar
  9. 9.
    Dubey, S.R., Jalal, A.S.: Robust approach for fruit and vegetable classification. Procedia Eng. 38, 3449–3453 (2012)CrossRefGoogle Scholar
  10. 10.
    Ninawe, P., Pandey, S.: A completion on fruit recognition system using k-nearest neighbors algorithm. Int. J. Adv. Res. Comput. Eng. Technol. 3(7), 2352–2356 (2014)Google Scholar
  11. 11.
    Haidar, A., Dong, H., Mavridis, N.: Image-based date fruit classification. In: 4th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), pp. 357–363. IEEE (2012)Google Scholar
  12. 12.
    Jana, S., Parekh, R.: Intra-class recognition of fruits using color and texture features with neural classifiers. Int. J. Comput. Appl. 148(11), 1–6 (2016)Google Scholar
  13. 13.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979). IEEECrossRefMathSciNetGoogle Scholar
  14. 14.
    Computers and Optics in Food Inspection. http://www.cofilab.com/portfolio. Accessed 15 Dec 2016

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.School of Education TechnologyJadavpur UniversityKolkataIndia

Personalised recommendations