Skip to main content
Springer Nature Link
Log in

Enhanced Residual Connections Method for Low Resolution Images in Rice Plant Disease Classification

  • Conference paper
  • First Online:
Computational Intelligence in Data Science (ICCIDS 2022)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 654))

Included in the following conference series:

  • 398 Accesses

Abstract

Recent advancements in both raw computing powers as well as the capabilities of cameras in recent times, it has now become possible to capture images of very high quality. This improvement however does come at the cost of the overall space required to store such high-quality images. One possible solution to this problem would be the storage of the images in low resolution and then upsampling the images to obtain the original resolutions. Despite advances in computer vision, deep learning models for super resolution have been introduced to address the challenges and thus provide promising improved performance results. This paper will explore a novel self-supervised deep learning architecture entitled Enhanced Residual Connections for Image super resolution (ERCSR) that is capable of upsampling extremely low-quality images to their higher quality. Experiments on the different data sets such as DIV2k and rice plant images are made to evaluate this model and experimental results shows that our method outperforms image enhancement. Furthermore, rice plant images are subsequently passed through disease classification layers and achieves desired accuracy for super resolution images.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Freeman, W.T., Jones, T.R., Pasztor, E.C.: Example-based super-resolution. IEEE Comput. Graph. Appl. 22(2), 56–65 (2002)

    Article  Google Scholar 

  2. Freedman, G., Fattal, R.: Image and video upscaling from local self-examples. ACM Trans. Graph. (TOG) 30(2), 1–11 (2011)

    Article  Google Scholar 

  3. Wang, X., et al.: ESRGAN: enhanced super-resolution generative adversarial networks. In: Leal-Taixé, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11133, pp. 63–79. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11021-5_5

  4. Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, 27 (2014)

    Google Scholar 

  5. Lugmayr, A., Danelljan, M., Timofte, R.: Unsupervised learning for real-world super-resolution. In: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 3408–3416. IEEE (2019)

    Google Scholar 

  6. Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2015)

    Article  Google Scholar 

  7. Dong, C., Loy, C.C., Tang, X.: Accelerating the super-resolution convolutional neural network. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 391–407. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_25

  8. Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1646–1654 (2016)

    Google Scholar 

  9. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  10. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  11. Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2472–2481 (2018)

    Google Scholar 

  12. Lim, B., Son, S., Kim, H., Nah, S., Mu Lee, K.: Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 136–144 (2017)

    Google Scholar 

  13. Tai, Y., Yang, J., Liu, X.: Image super-resolution via deep recursive residual network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3147–3155 (2017)

    Google Scholar 

  14. Ahn, N., Kang, B., Sohn, K.-A.: Fast, accurate, and lightweight super-resolution with cascading residual network. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11214, pp. 256–272. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01249-6_16

    Chapter  Google Scholar 

  15. Ledig, C., et al.: Photo realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4681–4690 (2017)

    Google Scholar 

  16. Keys, R.: Cubic convolution interpolation for digital image processing. IEEE Trans. Acoust. Speech Signal Process. 29(6), 1153–1160 (1981)

    Article  MathSciNet  Google Scholar 

  17. Zhang, K., Zuo, W., Zhang, L.: Learning a single convolutional super-resolution network for multiple degradations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3262–3271 (2018)

    Google Scholar 

  18. Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 184–199. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10593-2_13

  19. Kim, J., Lee, J.K., Lee, K.M.: Deeply-recursive convolutional network for image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1637–1645 (2016)

    Google Scholar 

  20. Nah, S., Hyun Kim, T., Mu Lee, K.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3883–3891 (2017)

    Google Scholar 

  21. Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-first AAAI conference on Artificial Intelligence (2017)

    Google Scholar 

  22. Timofte, R., Gu, S., Wu, J., Van Gool, L.: Ntire 2018 challenge on single image super-resolution: methods and results. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 852–863 (2018)

    Google Scholar 

  23. Huang, J.B., Singh, A., Ahuja, N.: Single image super-resolution from transformed self-exemplars. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5197–5206 (2015)

    Google Scholar 

  24. Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, vol. 2, pp. 416–423. IEEE (2001)

    Google Scholar 

  25. Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19(11), 2861–2873 (2010)

    Article  MathSciNet  Google Scholar 

  26. Wang, Z., Chen, J., Hoi, S.C.: Deep learning for image super-resolution: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 43(10), 3365–3387 (2020)

    Article  Google Scholar 

  27. Sethy, P.K., Barpanda, N.K., Rath, A.K., Behera, S.K.: Deep feature-based rice leaf disease identification using support vector machine. Comput. Electron. Agric. 175, 105527 (2020)

    Article  Google Scholar 

  28. Prajapati, H.B., Shah, J.P., Dabhi, V.K.: Detection and classification of rice plant diseases. Intell. Decis. Technol. 11(3), 357–373 (2017)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Coimbatore Institute of Technology, Coimbatore, 641014, India

    K. Sathya & M. Rajalakshmi

Authors
  1. K. Sathya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Rajalakshmi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. Sathya .

Editor information

Editors and Affiliations

  1. Sri Sivasubramaniya Nadar College of Engineering, Chennai, India

    Lekshmi Kalinathan

  2. Sri Sivasubramaniya Nadar College of Engineering, Chennai, India

    Priyadharsini R.

  3. Sri Sivasubramaniya Nadar College of Engineering, Chennai, India

    Madheswari Kanmani

  4. Sri Sivasubramaniya Nadar College of Engineering, Chennai, India

    Manisha S.

Rights and permissions

Reprints and permissions

Copyright information

© 2022 IFIP International Federation for Information Processing

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sathya, K., Rajalakshmi, M. (2022). Enhanced Residual Connections Method for Low Resolution Images in Rice Plant Disease Classification. In: Kalinathan, L., R., P., Kanmani, M., S., M. (eds) Computational Intelligence in Data Science. ICCIDS 2022. IFIP Advances in Information and Communication Technology, vol 654. Springer, Cham. https://doi.org/10.1007/978-3-031-16364-7_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-16364-7_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16363-0

  • Online ISBN: 978-3-031-16364-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships