The fruit classification algorithm based on the multi-optimization convolutional neural network | Multimedia Tools and Applications Skip to main content
Springer Nature Link
Log in

The fruit classification algorithm based on the multi-optimization convolutional neural network

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

Abstract

To solve the problems of the traditional convolutional neural network’s needs of long training time and poor accuracy in the process of fruit image classification, the present study proposes a fruit image classification method based on the multi-optimization convolutional neural network with the background of fruit classification. Firstly, in order to avoid the interference of external noise and influence the accuracy of classification, the wavelet threshold is used to denoise the fruit image, which can reduce image noise while preserving the details of the image. Secondly, to correct the over-bright fruit image or the over-dark fruit image, the gamma transform is adopted to correct the image. Finally, in the process of constructing the convolutional neural network, the SOM network is introduced for pre-learning the samples. Besides, the weights of the trained optimal SOM network are applied to the full connection layer, and an integrated optimization model of convolution and full connection is established for feature extraction and regression classification. The optimized convolutional neural network was adopted to classify fruits. According to the application results, the accuracy of the optimized convolutional neural network for fruit classification reaches 99%. Therefore, the improved convolutional neural network depth learning algorithm makes better performance to achieve fruit classification.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Adeel A, Khan MA, Akram T, Sharif A, Javed K (2020) Entropy controlled deep features selection framework for grape leaf diseases recognition. Expert Syst. https://doi.org/10.1111/exsy.12569

  2. Alishba A, Muhammad, AK, Muhammad, S, et al. (2019) Diagnosis and recognition of grape leaf diseases: an automated system based on a novel saliency approach and canonical correlation analysis based multiple features fusion. Sustainable Computing: Informatics and Systems, 24. https://doi.org/10.1016/j.suscom.2019.08.002

  3. Aurangzeb K, Akmal F, Khan MA, Sharif M, Javed MY (2020) Advanced machine learning algorithm based system for crops leaf diseases recognition. In 2020 6th Conference on Data Science and Machine Learning Applications (CDMA). IEEE:146–151. https://doi.org/10.1109/CDMA47397.2020.00031

  4. Chaitali G (2015) View on normal and affected fruit classification. International Journal on Recent and Innovation Trends in Computing and Communication

  5. Chun W, Hu YJ, Miao., Min LI (2005) The Processing Methods and Estimate of Noised Image. J Astronautic Metrol Measurement

  6. Ghaseminezhad MH, Karami A (2011) A novel self-organizing map (SOM) neural network for discrete groups of data clustering. Applied Soft Computing 11(4):3771–3778. https://doi.org/10.1016/j.asoc.2011.02.009

    Article  Google Scholar 

  7. Haidar A, Haiwei D, Nikolaos M (2012) Image-based date fruit classification. 2012 IV International Congress on Ultra Modern Telecommunications and Control Systems, Russia. IEEE, 357–363. https://doi.org/10.1109/icumt.2012.6459693

  8. He W (2015) Enhancement and denoising method of medical ultrasound image based on wavelet transform and fuzzy theory. Electronic Design Eng (12):101–104. https://doi.org/10.3969/j.issn.1674-6236.2015.12.031

  9. Hossain MS, Al-Hammadi M, Muhammad G (2019) Automatic fruit classification using deep learning for industrial applications. IEEE Trans Ind Informat 15(2):1027–1034. https://doi.org/10.1109/TII.2018.2875149

  10. Hu J, Luo YY, Jianhong N (2014) Design of a Fruit Classification System. J Shanghai Dianji Univ. https://doi.org/10.3969/j.issn.2095-0020.2014.06.007

  11. Jia SJ, Yang DP, Liu JH (2014) Product image fine-grained classification based on convolutional neural network. J Shandong Univ Sci Technol (Natural Science) 33(6):91–96. https://doi.org/10.3969/j.issn.1672-3767.2014.06.013

    Article  Google Scholar 

  12. Kang H, Chen C (2019) Fast implementation of real-time fruit detection in apple orchards using deep learning. Comput Electron Agric 168. https://doi.org/10.1016/j.compag.2019.105108

  13. Khan MA, Akram T, Sharif M, Awais M, Javed K, Ali H et al (2018) Ccdf: automatic system for segmentation and recognition of fruit crops diseases based on correlation coefficient and deep cnn features. Comput Electron Agric 155:220–236. https://doi.org/10.1016/j.compag.2018.10.013

    Article  Google Scholar 

  14. Khan MA, Lali MI, Sharif M, Javed K, Aurangzeb K, Haider SI, et al. (2019) An optimized method for segmentation and classification of apple diseases based on strong correlation and genetic algorithm based feature selection. IEEE Access:46261–46277. https://doi.org/10.1109/ACCESS.2019.2908040

  15. Khan MA, Akram T, Sharif M, Saba T (2020) Fruits diseases classification: exploiting a hierarchical framework for deep features fusion and selection. Multimed Tools Appl, 25763–25783. https://doi.org/10.1007/s11042-020-09244-3

  16. Li Y (2016) Saliency detection based on deep convolutional neural network. J Image Graphics 21(1):53–59. https://doi.org/10.11834/jig.20160107

    Article  Google Scholar 

  17. Li Y, Jing XX, Yang HY (2014) Speech de-noising method based on empirical mode decomposition and improved wavelet threshold. Comput Eng Design. https://doi.org/10.2495/ciie140371

  18. Liu JF (2014) A Concise and Efficient Method for Accelerating Convolution Neural Networks. Sci Technol Eng. https://doi.org/10.3969/j.issn.1671-1815.2014.33.045

  19. Lu HT, Zhang QC (2016) Applications of deep convolutional neural network in computer vision. J Data Acquis Process 31(01):1–17

    Google Scholar 

  20. Osako Y, Yamane H, Lin SY, Chen PA, Tao R (2020) Cultivar discrimination of litchi fruit images using deep learning. Scientia Horticulturae 269:109360. https://doi.org/10.1016/j.scienta.2020.109360

    Article  Google Scholar 

  21. Reza FR, Kinsner K (2007) Image decomposition and reconstruction using two-dimensional complex-valued Gabor wavelets. 6th IEEE International Conference on Cognitive Informatics, Lake Tahoo, CA, USA. IEEE:72–78. https://doi.org/10.1109/coginf.2007.4341874

  22. Safdar, A., Khan, M. A, Shah, J. H., Sharif, M., junaid.ali@hitecuni.edu.pk. (2019) Intelligent microscopic approach for identification and recognition of citrus deformities. Microscopy Res Tech 82(2). https://doi.org/10.1002/jemt.23320

  23. Srivastava M, Anderson CL, Freed JH (2016) A new wavelet denoising method for selecting decomposition levels and noise thresholds. IEEE Access 4:3862–3877. https://doi.org/10.1109/access.2016.2587581

    Article  Google Scholar 

  24. Tao H, Zhao L, Xi J, Yu L, Wang T (2014) Fruits and vegetables recognition based on color and texture features. Trans Chinese Soc Agric Eng 30(16):305–311. https://doi.org/10.3969/j.issn.1002-6819.2014.16.039

    Article  Google Scholar 

  25. Wang XH, Zhao ZX (2016) PCA face recognition algorithm combined with gamma transform and wavelet transform. Computer Engineering and Applications, 2016

  26. Wu GW, Wang CM, Bao JD, Chen Y, Hu YP (2014) A wavelet threshold de-noising algorithm based on adaptive threshold function. J Electron Information Technol 36(6):1340–1347

    Google Scholar 

  27. Wu MG, Zheng PB, Cui LL (2015) Gamut Mapping of Electronic Map Based on SOM Neural Network. Acta Electronica Sinica 43(6):1108–1112

    Google Scholar 

  28. Xu LJ (2012) Color difference in strawberry classification Algorithm of maturity of the application of the test. Bull Sci Technol 28(10):160–162. https://doi.org/10.13774/j.cnki.kjtb.2012.10.052

  29. Zahid I, Attique KM, Muhammad S, Hussain SJ, Habib URM, Kashif J (2018) An automated detection and classification of citrus plant diseases using image processing techniques: a review. Comput Electron Agric 153:12–32. https://doi.org/10.1016/j.compag.2018.07.032

    Article  Google Scholar 

  30. Zhong JJ, Song J, You C, Yin X (2015) Wavelet de-noising method with threshold selection rules based on SNR evaluations. J Tsinghua Univ (Sci Technol) 54(2):259–263

    Google Scholar 

  31. Zhou FX, Wang RX, Xie DJ, Wang WQ (2019) Network intrusion detection method with improved 3-som. Modern Electronics Technique 042(015):68–71 78

    Google Scholar 

Download references

Acknowledgements

This work was supported by The State Bureau of Forestry “948” project in China (Grant No. 2014-4-09), the National Natural Science Foundation of China (Grant No. 61703441).

Author information

Authors and Affiliations

  1. College of Computer & Information Engineering, Central South University of Forestry and Technology, Changsha, 410004, China

    Xiao Chen, Guoxiong Zhou, Aibin Chen, Ling Pu & Wenjie Chen

  2. Central South University of Forest & Technology, Changsha, 410000, Hunan, China

    Guoxiong Zhou

Authors
  1. Xiao Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guoxiong Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aibin Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ling Pu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wenjie Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guoxiong Zhou.

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

Chen, X., Zhou, G., Chen, A. et al. The fruit classification algorithm based on the multi-optimization convolutional neural network. Multimed Tools Appl 80, 11313–11330 (2021). https://doi.org/10.1007/s11042-020-10406-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-10406-6

Keywords

Search

Navigation