Skip to main content
SpringerLink
Log in

Four Fold Prolonged Residual Network (FFPRN) Based Super Resolution for Cherry Plant Leaf Disease Detection

  • Conference paper
  • First Online:
Intelligent Control, Robotics, and Industrial Automation (RCAAI 2022)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1066))

Abstract

The cherry cultivation has been threatened by crop loss due to the diseases from numerous sources such as insects, bacteria, viruses, and fungi. In order to diagnose the disease in its early stage, a novel Four Fold Prolonged Residual Network (FFPRN)-based super resolution for cherry plant leaf disease detection has been proposed in this paper. The proposed method extracts the deep features through four folds to create a high-resolution image from a given low-resolution cherry leaf image. This paper uses the plant village cherry leaf dataset to compare the performance of proposed model to Resnet50, Googlenet and Alexnet. For the super resolution factor 2 proposed model outperformed the existing Resnet50, Googlenet and Alexnet models. For super resolution factors 2,4,6 the proposed FFPRN model achieves PSNR values of 32.9305, 32.3306, 31.2962 and SSIM values of 0.8407, 0.8298, 0.8119 and classification accuracy values of 99.48, 99.08, 98.83, respectively.

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
Hardcover Book
USD 279.99
Price excludes VAT (USA)
  • Durable hardcover 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

Similar content being viewed by others

References

  1. Nazki H, Yoon S, Fuentes A, Park DS (2020) Unsupervised image translation using adversarial networks for improved plant disease recognition. Comput Electron Agric 168:105117. https://doi.org/10.1016/J.COMPAG.2019.105117

    Article  Google Scholar 

  2. Lacey LA, Grzywacz D, Shapiro-Ilan DI, Frutos R, Brownbridge M, Goettel MS (2015) Insect pathogens as biological control agents: back to the future. J Invertebr Pathol 132:1–41. https://doi.org/10.1016/J.JIP.2015.07.009

    Article  Google Scholar 

  3. Gajjar R, Gajjar N, Thakor VJ, Patel NP, Ruparelia S (2022) Real-time detection and identification of plant leaf diseases using convolutional neural networks on an embedded platform. Visual Comput 38(8):2923–2938. https://doi.org/10.1007/S00371-021-02164-9/FIGURES/12

    Article  Google Scholar 

  4. Zhang K, Zhang L, Wu Q, Identification of cherry leaf disease infected by Podosphaera Pannosa via convolutional neural network 10(2):98–110. https://services.igi-globl.com/resolvedoi/resolve.aspx? 1AD. https://doi.org/10.4018/IJAEIS.2019040105

  5. Bock CH, Poole GH, Parker PE, Gottwald TR (2010) Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging 29(2):59–107 https://doi.org/10.1080/07352681003617285

  6. Abiodun OI, Jantan A, Omolara AE, Dada KV, Mohamed NAE, Arshad H (2018) State-of-the-art in artificial neural network applications: a survey. Heliyon 4(11):e00938. https://doi.org/10.1016/J.HELIYON.2018.E00938

    Article  Google Scholar 

  7. Patrício DI, Rieder R (2018) Computer vision and artificial intelligence in precision agriculture for grain crops: a systematic review. Comput Electron Agric 153:69–81. https://doi.org/10.1016/J.COMPAG.2018.08.001

    Article  Google Scholar 

  8. Zhang T, Zheng W, Cui Z, Zong Y, Yan J, Yan K (2016) A deep neural network-driven feature learning method for multi-view facial expression recognition. IEEE Trans Multimedia 18(12):2528–2536. https://doi.org/10.1109/TMM.2016.2598092

    Article  Google Scholar 

  9. Rouse MN, Nava IC, Chao S, Anderson JA, Jin Y (2012) Identification of markers linked to the race Ug99 effective stem rust resistance gene Sr28 in wheat (Triticum aestivum L.). Theor Appl Genet 125(5):877–885. https://doi.org/10.1007/S00122-012-1879-6/FIGURES/3

  10. Dananjayan S, Tang Y, Zhuang J, Hou C, Luo S (2022) Assessment of state-of-the-art deep learning based citrus disease detection techniques using annotated optical leaf images. Comput Electron Agric 193:106658. https://doi.org/10.1016/J.COMPAG.2021.106658

    Article  Google Scholar 

  11. Asif MKR, Rahman MA, Hena MH (2020) CNN based disease detection approach on potato leaves. In: Proceedings of the 3rd international conference on intelligent sustainable systems, ICISS 2020, 428–432. https://doi.org/10.1109/ICISS49785.2020.9316021

  12. Harris JL (1964) Resolving power and decision theory*†. JOSA 54(5):606–611. https://doi.org/10.1364/JOSA.54.000606

    Article  Google Scholar 

  13. Huang TS (n.d.) Advances in computer vision and image processing | Guide books. Retrieved November 21, 2022, from https://dl.acm.org/doi/abs/https://doi.org/10.5555/61892

  14. Tom BC, Katsaggelos AK (1996) Reconstruction of a high-resolution image by simultaneous registration, restoration, and interpolation of low-resolution images. IEEE Int Conf Image Process 2:539–542. https://doi.org/10.1109/ICIP.1995.537535

    Article  Google Scholar 

  15. Haris M, Shakhnarovich G, Ukita N (2018) Deep back-projection networks for super-resolution, pp 1664–1673

    Google Scholar 

  16. Zhou S, Zhang J, Zuo W, Loy CC (2020) Cross-scale internal graph neural network for image super-resolution. Adv Neural Inf Process Syst 33:3499–3509. https://github.com/sczhou/IGNN

  17. Capizzi G, Sciuto Gl, Napoli C, Tramontana E, Woniak M (2016) A novel neural networks-based texture image processing algorithm for orange defects classification. Int J Comput Sci Appl 13(2):45–60. https://www.researchgate.net/publication/309769648

  18. Singh UP, Chouhan SS, Jain S, Jain S (2019) Multilayer convolution neural network for the classification of mango leaves infected by anthracnose disease. IEEE Access 7:43721–43729. https://doi.org/10.1109/ACCESS.2019.2907383

    Article  Google Scholar 

  19. Cruz AC, Luvisi A, de Bellis L, Ampatzidis Y (2017) X-FIDO: An effective application for detecting olive quick decline syndrome with deep learning and data fusion. Frontiers Plant Sci 8:1741. https://doi.org/10.3389/FPLS.2017.01741/BIBTEX

    Article  Google Scholar 

  20. Pawar R, Jadhav A (2018) Pomogranite disease detection and classification. In: IEEE international conference on power, control, signals and instrumentation engineering, ICPCSI 2017, 2475–2479.https://doi.org/10.1109/ICPCSI.2017.8392162

  21. Jiang F, Lu Y, Chen Y, Cai D, Li G (2020) Image recognition of four rice leaf diseases based on deep learning and support vector machine. Comput Electron Agric 179:105824. https://doi.org/10.1016/J.COMPAG.2020.105824

    Article  Google Scholar 

  22. Ramcharan A, McCloskey P, Baranowski K, Mbilinyi N, Mrisho L, Ndalahwa M, Legg J, Hughes DP (2019) A mobile-based deep learning model for cassava disease diagnosis. Frontiers Plant Sci 10:272. https://doi.org/10.3389/FPLS.2019.00272

    Article  Google Scholar 

  23. Hu G, Yang X, Zhang Y, Wan M (2019) Identification of tea leaf diseases by using an improved deep convolutional neural network. Sustain Comput: Inf Syst 24:100353. https://doi.org/10.1016/J.SUSCOM.2019.100353

    Article  Google Scholar 

  24. Lu H, Yang R, Deng Z, Zhang Y, Gao G, Lan R (2021) Chinese image captioning via fuzzy attention-based DenseNet-BiLSTM. ACM Trans Multimedia Comput Commun Appl (TOMM) 17(1s). https://doi.org/10.1145/3422668

  25. Karthik R, Hariharan M, Anand S, Mathikshara P, Johnson A, Menaka R (2020) Attention embedded residual CNN for disease detection in tomato leaves. Appl Soft Comput 86:105933. https://doi.org/10.1016/J.ASOC.2019.105933

    Article  Google Scholar 

  26. Yeswanth PV, Khandelwal R, Deivalakshmi S (2023) Super resolution-based leaf disease detection in potato plant using broad deep residual network (BDRN). SN Comput Sci 4(2). https://doi.org/10.1007/s42979-022-01514-1

  27. Yeswanth PV, Khandelwal R, Deivalakshmi S, Muttukrishnan RM, Rajarajan M, Veeravalli B, Kesswani N, Patel A (2023) Internet of things (IoT): key digital trends shaping the future. In: Proceedings of 7th International Conference on Internet of Things and Connected Technologies (ICIoTCT 2022) Two Fold Extended Residual Network Based Super Resolution for Potato Plant Leaf Disease Detection Springer Nature Singapore Singapore pp 197–209

    Google Scholar 

  28. Yeswanth PV, Kushal S, Tyagi G, Kumar MT, Deivalakshmi S, Ramasubramanian SP (2023) Iterative super resolution network (ISNR) for potato leaf disease detection. In: 2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), Karaikal, India. pp 1–6. https://doi.org/10.1109/IConSCEPT57958.2023.10170224

  29. Yeswanth PV, Deivalakshmi S, George S, Ko SB (2023) Residual skip network-based super-resolution for leaf disease detection of grape plant. Circ Syst Sig Process. https://doi.org/10.1007/s00034-023-02430-2

  30. Yeswanth PV, Deivalakshmi S (2023) Extended wavelet sparse convolutional neural network (EWSCNN) for super resolution Sādhanā 48(2). https://doi.org/10.1007/s12046-023-02108-0

  31. Yeswanth PV, Raviteja R, Deivalakshmi S (2023) Sovereign critique network (SCN) based super-resolution for chest X-rays images. In: 2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), Karaikal, India, pp 1–5. https://doi.org/10.1109/IConSCEPT57958.2023.10170157

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology, Tiruchirappalli, India

    P. V. Yeswanth, Rachit Khandelwal & S. Deivalakshmi

Authors
  1. P. V. Yeswanth
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rachit Khandelwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. Deivalakshmi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to P. V. Yeswanth .

Editor information

Editors and Affiliations

  1. School of Engg., Computing and Math.,, University of Plymouth, Plymouth, UK

    Sanjay Sharma

  2. School of Electrical Sciences, Indian Institute of Technology Goa, Ponda, Goa, India

    Bidyadhar Subudhi

  3. Department of Mechatronics, Manipal Institute of Technology, Manipal, Karnataka, India

    Umesh Kumar Sahu

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yeswanth, P.V., Khandelwal, R., Deivalakshmi, S. (2023). Four Fold Prolonged Residual Network (FFPRN) Based Super Resolution for Cherry Plant Leaf Disease Detection. In: Sharma, S., Subudhi, B., Sahu, U.K. (eds) Intelligent Control, Robotics, and Industrial Automation. RCAAI 2022. Lecture Notes in Electrical Engineering, vol 1066. Springer, Singapore. https://doi.org/10.1007/978-981-99-4634-1_38

Download citation

Publish with us

Policies and ethics

Search

Navigation