Skip to main content
SpringerLink
Log in

Flower Recognition Using VGG16

  • Conference paper
  • First Online:
Third International Conference on Image Processing and Capsule Networks (ICIPCN 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 514))

Included in the following conference series:

Abstract

The purpose of our model is to classify five types of flowers from input images. The flowers are Sunflower, Rose, Tulip, Daisy, and Lavender. We have also built our own CNN model for the task and compared it with the modified VGG16 network. Our modified VGG16 model gives better accuracy than the existing works. We have achieved a test accuracy of 96.64% by using the proposed model. As the accuracy is quite good, we were able to recognize the flowers accurately. Agriculture institutes and flower nurseries can be benefitted by using this model.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 279.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Le, T.L., Duong, N.D., Vu, H., Nguyen, T.N.: MICA at LifeCLEF 2015: multi-organ plant identification. In: CEUR Workshop Proceedings, vol. 1391 (2015)

    Google Scholar 

  2. Nilsback, M.: An automatic visual Flora – segmentation and classification of flower images. Thesis, p. 20 (2009)

    Google Scholar 

  3. Rodrigo, R., Samarawickrame, K., Mindya, S.: An intelligent flower analyzing system for medicinal plants. In: 21st International Conference on Central Europe on Computer Graphics, Visualization and Computer Vision in Co-operation with EUROGRAPHICS Association, WSCG 2013 - Poster Proceedings, pp. 41–44 (2013)

    Google Scholar 

  4. Hong, A.-X., Chen, G., Li, J., Chi, Z., Zhang, D.: A flower image retrieval method based on ROI feature. J. Zhejiang Univ. Science A 5(7), 764–772 (2004). https://doi.org/10.1631/jzus.2004.0764

    Article  Google Scholar 

  5. Goëau, H., Bonnet, P., Joly, A.: LifeCLEF plant identification task 2015. In: CEUR Workshop Proceedings, vol. 1391 (2015)

    Google Scholar 

  6. Bo, L., Ren, X., Fox, D.: Kernel descriptors for visual recognition. In: Advances in Neural Information Processing Systems, 24th Annual Conference on Neural Information Processing Systems, NIPS 2010, vol. 23, June 2010

    Google Scholar 

  7. Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 7–12 June 2015, pp. 1–9, October 2015. https://doi.org/10.1109/CVPR.2015.7298594

  8. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Adv. Neural Inform. Proc. Syst. 25 (NIPS 2012)

    Google Scholar 

  9. Thanh, T., et al.: Flower species identification using deep convolutional neural networks. In: AUN/SEED-Net Regional Conference for Computer and Information Engineering 2016 (RCCIE 2016), p. 6 (2016)

    Google Scholar 

  10. Ranganathan, G.: A study to find facts behind preprocessing on deep learning algorithms. J. Innov. Image Process. 3(1), 66–74 (2021). https://doi.org/10.36548/jiip.2021.1.006

  11. Sungheetha, A., Sharma R.R.: Classification of remote sensing image scenes using double feature extraction hybrid deep learning approach. J. Inf. Technol. Digit. World 3(2), 133–149 (2021). https://doi.org/10.36548/jitdw.2021.2.006

  12. Lodh, A., Parekh, R.: Flower recognition system based on color and GIST features. In: Proceedings of 2nd International Conference on Devices for Integrated Circuit (DevIC) 2017, pp. 790–794 (2017). https://doi.org/10.1109/DEVIC.2017.8074061

  13. Patel, R., Panda, C.S.: A review on flower image recognition. Int. J. Comput. Sci. Eng. 7(10), 206–216, (2019). https://doi.org/10.26438/ijcse/v7i10.206216

  14. Lv, R., Li, Z., Zuo, J., Liu, J.: Flower classification and recognition based on significance test and transfer learning. In: 2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE), pp. 649–652 (2021). https://doi.org/10.1109/ICCECE51280.2021.9342468

  15. Vijayakumar, T.: Synthesis of palm print in feature fusion techniques for multimodal biometric recognition system online signature. J. Innov. Image Process. 3(2), 131–143 (2021). https://doi.org/10.36548/jiip.2021.2.005

  16. Liu, F., Wang, Y., Wang, F.C., Zhang, Y.Z., Lin, J.: Intelligent and secure content-based image retrieval for mobile users. IEEE Access 7, 119209–119222 (2019). https://doi.org/10.1109/ACCESS.2019.2935222

  17. Gogul, I., Kumar, V.S.: Flower species recognition system using convolution neural networks and transfer learning. In: 2017 4th International Conference on Signal Processing, Communication and Networking, ICSCN 2017, pp. 1–6 (2017). https://doi.org/10.1109/ICSCN.2017.8085675

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, 1212, Bangladesh

    Md. Ashikur Rahman, Md. Saif Laskar, Samir Asif & Ahmed Wasif Reza

  2. Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh

    Omar Tawhid Imam

  3. Department of CSE, Daffodil International University, Dhaka, 1341, Bangladesh

    Mohammad Shamsul Arefin

  4. Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chattogram, 4349, Bangladesh

    Mohammad Shamsul Arefin

Authors
  1. Md. Ashikur Rahman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Md. Saif Laskar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Samir Asif
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Omar Tawhid Imam
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ahmed Wasif Reza
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mohammad Shamsul Arefin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Shamsul Arefin .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Da-Yeh University, Changhua, Taiwan

    Joy Iong-Zong Chen

  2. Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

  3. Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA

    Fuqian Shi

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Rahman, M., Laskar, M., Asif, S., Imam, O.T., Reza, A.W., Arefin, M.S. (2022). Flower Recognition Using VGG16. In: Chen, J.IZ., Tavares, J.M.R.S., Shi, F. (eds) Third International Conference on Image Processing and Capsule Networks. ICIPCN 2022. Lecture Notes in Networks and Systems, vol 514. Springer, Cham. https://doi.org/10.1007/978-3-031-12413-6_59

Download citation

Publish with us

Policies and ethics

Search

Navigation