Skip to main content
SpringerLink
Log in

Hybrid classifier model for fruit classification

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

With an advancement in artificial intelligence (AI) applications, the use of smart imaging devices has been increased at a rapid rate. Recently, many researchers have utilized deep learning models such as convolutional neural networks (CNN) for image classification models. Compared to the traditional machine learning models, CNN does not require any kind of handcrafted features. It utilizes various filters to extract the potential features of images automatically. Inspired from this, in this paper, we have proposed a novel fruit classification model which utilizes the features of CNN, Long short Term Memory (LSTM) and Recurrent Neural Network (RNN) architectures. Type-II fuzzy enhancement is also used as pre-processing tool to enhance the images. Additionally, to tune the hyper-parameters of the proposed model, TLBO-MCET is also utilized. Extensive experiments are drawn by considering the existing and the proposed fruit classification models. Comparative analysis reveals that the proposed model outperforms the competitive fruit classification models.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23

Similar content being viewed by others

References

  1. ANDRECUT M (1999) A statistical-fuzzy perceptron. Modern Physics Letters B 13(01):33–41

    Article  Google Scholar 

  2. Dang C, Gao J, Wang Z, Chen F, Xiao Y (2015) Multi-step radiographic image enhancement conforming to weld defect segmentation. IET Image Process 9(11):943–950

    Article  Google Scholar 

  3. Deng H, Sun X, Liu M, Ye C, Zhou X (2016) Image enhancement based on intuitionistic fuzzy sets theory. IET Image Process 10(10):701–709

    Article  Google Scholar 

  4. Deng H, Sun X, Liu M, Ye C, Zhou X (2016) Image enhancement based on intuitionistic fuzzy sets theory. IET Image Process 10(10):701–709

    Article  Google Scholar 

  5. Fang C, Mu D, Deng Z, Yan J (2017) Uncovering the fuzzy community structure accurately based on steepest descent projection. Modern Physics Letters B 31(27):1750249

    Article  Google Scholar 

  6. Gill H S, Khehra B S, Singh A, Kaur L (2019) Teaching-learning-based optimization algorithm to minimize cross entropy for selecting multilevel threshold values. Egyptian Info J 20(1):11–25

    Article  Google Scholar 

  7. Gill HS, Khehra BS (2020) Efficient image classification technique for weather degraded fruit images, IET Image Processing

  8. Guo Y, Liu Y, Bakker EM, Guo Y, Lew MS (2018) cnn-rnn:a large-scale hierarchical image classification framework. Multi Tools App 77(8):10251–10271

    Article  Google Scholar 

  9. Hautire N, Tarel JP, Aubert D, Dumont E (2011) Blind contrast enhancement assessment by gradient ratioing at visible edges. Image Anal Stereology 27(2):87–95

    Article  MathSciNet  Google Scholar 

  10. Horng M-H (2010) Multilevel minimum cross entropy threshold selection based on the honey bee mating optimization. Expert Syst Appl 37(6):4580–4592

    Article  Google Scholar 

  11. Horng MH, Liou RJ (2011) Multilevel minimum cross entropy threshold selection based on the firefly algorithm. Expert Syst Appl 38(12):14805–14811

    Article  Google Scholar 

  12. Kalyani R, Sathya PD, Sakthivel VP (2020) Trading strategies for image segmentation using multilevel thresholding aided with minimum cross entropy. Engineering Science and Technology, an International Journal

  13. Li C, Yang Y, Xiao L, Li Y, Zhou Y, Zhao J (2016) A novel image enhancement method using fuzzy sure entropy. Neurocomputing 215:196–211

    Article  Google Scholar 

  14. Liu W, Chen X, Chu X, Wu Y, Lv J (2016) Haze removal for a single inland waterway image using sky segmentation and dark channel prior. IET Image Process 10(12):996–1006

    Article  Google Scholar 

  15. Mohanty B, Tripathy S (2016) A teaching learning based optimization technique for optimal location and size of dg in distribution network. J Electrical Sys Info Tech 3(1):33–44

    Article  Google Scholar 

  16. Nagadurga T, Narasimham PVRL, Vakula VS (2020) Global maximum power point tracking of solar pv strings using the teaching learning based optimisation technique. Inter J Ambient Energy, pp 1–12

  17. Naji AA, Lee SH, Chahl J (2017) Quality index evaluation of videos based on fuzzy interface system. IET Image Process 11(5):292–300

    Article  Google Scholar 

  18. Nie F, Gao C, Guo Y, Gan M (2011) Two-dimensional minimum local cross-entropy thresholding based on co-occurrence matrix. Comp Electrical Eng 37(5):757–767

    Article  Google Scholar 

  19. Rao RV, Patel V (2013) An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems. Scientia Iranica 20(3):710–720

    Google Scholar 

  20. Rao RV, Savsani VJ, Vakharia DP (2012) Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems. Inf Sci 183(1):1–15

    Article  MathSciNet  Google Scholar 

  21. Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315

    Article  Google Scholar 

  22. Rasti P, Nasrollahi K, Orlova O, Tamberg G, Ozcinar C, Moeslund TB, Anbarjafari G (2017) A new low-complexity patch-based image super-resolution. IET Comput Vis 11(7):567–576

    Article  Google Scholar 

  23. Russ JC (2016) The image processing handbook, CRC press, North Carolina State University, Materials Science and Engineering Department Raleigh, North Carolina, pp 1–85

  24. Shi JP (2017) Prediction study on the degeneration of lithium-ion battery based on fuzzy inference system. Modern Physics Letters B 31 (19-21):1740083–1740091

    Article  Google Scholar 

  25. Singh D, Kumar V (2017) Dehazing of remote sensing images using improved restoration model based dark channel prior. The Imaging Sci J 65 (5):282–292

    Article  Google Scholar 

  26. Singh D, Kumar V (2018) Single image haze removal using integrated dark and bright channel prior. Modern Physics Letters B 32(04):1850051–1850058

    Article  MathSciNet  Google Scholar 

  27. Singh D, Kumar V (2017) Modified gain intervention filter based dehazing technique. J Modern Optics 64(20):2165–2178

    Article  Google Scholar 

  28. Singh H, Khehra BS (2018) Visibility enhancement of color images using type-ii fuzzy membership function. Modern Physics Letters B 32(11):1850130

    Article  MathSciNet  Google Scholar 

  29. Tang K, Yuan X, Sun T, Yang J, Gao S (2011) An improved scheme for minimum cross entropy threshold selection based on genetic algorithm. Knowl-Based Syst 24(8):1131–1138

    Article  Google Scholar 

  30. Tian Z, Jia L-M, Dong HH, Zhang Z-D, Ye Y-D (2015) Fuzzy peak hour for urban road traffic network. Modern Physics Letters B 29(15):1550074

    Article  MathSciNet  Google Scholar 

  31. Tripathi AK, Mukhopadhyay S (2012) Removal of fog from images: A review. IETE Tech Rev 29(2):148–156

    Article  Google Scholar 

  32. Wang KL, Wang HB, Yu LX, Ma XY, Xue YS (2013) Teaching-learning-based optimization algorithm for dealing with real-parameter optimization problems. In: Applied mechanics and materials, vol 380, pp 1342–1345

  33. YOO WJ, JI DH, WON SC (2010) Adaptive fuzzy synchronization of two different chaotic systems with stochastic unknown parameters. Modern Physics Letters B 24(10):979–994

    Article  Google Scholar 

  34. Zhang J, Wu Q, Shen C, Zhang J, Lu J (2018) Multilabel image classification with regional latent semantic dependencies. IEEE Tran Multi 20(10):2801–2813

    Article  Google Scholar 

  35. Zheng L, Shi H, Gu M (2017) Infrared traffic image enhancement algorithm based on dark channel prior and gamma correction. Modern Physics Letters B 31(19-21):1740044–1740050

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science, Guru Arjan Dev Khalsa College, Chohla Sahib, Tarn Taran, Punjab, India

    Harmandeep Singh Gill

  2. CSE, BBSBEC, Fatehgarh Sahib, India

    Baljit Singh Khehra

Authors
  1. Harmandeep Singh Gill
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Baljit Singh Khehra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Harmandeep Singh Gill.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gill, H.S., Khehra, B.S. Hybrid classifier model for fruit classification. Multimed Tools Appl 80, 27495–27530 (2021). https://doi.org/10.1007/s11042-021-10772-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-021-10772-9

Keywords

Search

Navigation