SpringerLink
Log in

Plant Disease Detection and Severity Assessment Using Image Processing and Deep Learning Techniques

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

Efficient plant disease detection and severity assessment are crucial not just for agricultural purposes but also for global health, economics, as well as ecological sustainability. With the help of innovative computational techniques, we need to build resilient agricultural systems for a sustainable future. In this paper, firstly, the authors implemented four distinct image enhancement techniques. Based on the results, the technique with the best accuracy measures was selected for further implementation. Next, six CNN architectures namely AlexNet, ResNet18, ResNet50, ResNet101, SqueezeNet, and Inception V3 were implemented on an original image dataset constituting tomato early blight leaf images. Thereafter, image processing was performed on the images in order to enhance their quality and size. For disease detection, AlexNet, SqueezeNet, ResNet18, ResNet50, ResNet101, and Inception V3 achieved an accuracy of 96.43%, 97.32%, 99.11%, 99.55%, 97.32%, and 98.66%, respectively. Next, the images were divided into classes of disease severity, namely healthy, early, middle, and late, for which the accuracies achieved by all CNNs ranged between 66.88% and 78.98%. Next, the six CNN models were used only for feature extraction and SVM was applied for classification. The best accuracy of 82.80% was achieved via ResNet101 architecture. A similar implementation was done after performing segmentation on the images in the dataset.

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
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Data availability

The publicly available datasets analyzed during this study are available in the PlantVillage repository, also available at https://github.com/spMohanty/PlantVillage-Dataset. The original dataset generated and analysed during the current study is available from the corresponding author on reasonable request.

References

  1. Abdulridha J, Ampatzidis Y, Qureshi J, Roberts P. Identification and classification of downy mildew severity stages in watermelon utilizing aerial and ground remote sensing and machine learning. Front Plant Sci. 2022;13: 791018.

    Article  Google Scholar 

  2. Ansari AS, Jawarneh M, Ritonga M, Jamwal P, Mohammadi MS, Veluri RK, et al. Improved support vector machine and image processing enabled methodology for detection and classification of grape leaf disease. J Food Qual. 2022;2022:1–6.

    Article  Google Scholar 

  3. Bilal A, Shafiq M, Fang F, Waqar M, Ullah I, Ghadi YY, et al. IGWO-IVNet3: DL-based automatic diagnosis of lung nodules using an improved Gray Wolf optimization and InceptionNet-V3. Sensors. 2022;22(24):9603.

    Article  Google Scholar 

  4. Bilal A, Sun G, Li Y, Mazhar S, Latif J. Lung nodules detection using grey wolf optimization by weighted filters and classification using CNN. J Chin Inst Eng. 2022;45(2):175–86.

    Article  Google Scholar 

  5. Bilal A, Sun G, Mazhar S. Diabetic retinopathy detection using weighted filters and classification using CNN. In: 2021 International conference on intelligent technologies (CONIT). IEEE; 2021. p. 1–6.

  6. Bilal A, Sun G, Mazhar S, Imran A. Improved Grey Wolf optimization-based feature selection and classification using CNN for diabetic retinopathy detection. In: Evolutionary computing and mobile sustainable networks: proceedings of ICECMSN 2021. Singapore: Springer; 2022. p. 1–14.

    Google Scholar 

  7. Bilal A, Sun G, Mazhar S, Imran A, Latif J. A Transfer Learning and U-Net-based automatic detection of diabetic retinopathy from fundus images. Comput Methods Biomech Biomed Eng Imaging Vis. 2022;10(6):663–74.

    Article  Google Scholar 

  8. Bilal A, Sun G, Mazhar S, Junjie Z. Neuro-optimized numerical treatment of HIV infection model. Int J Biomath. 2021;14(05):2150033.

    Article  MathSciNet  MATH  Google Scholar 

  9. Bilal A, Zhu L, Deng A, Lu H, Wu N. AI-based automatic detection and classification of diabetic retinopathy using U-Net and deep learning. Symmetry. 2022;14(7):1427.

    Article  Google Scholar 

  10. Bilal A, Sun G, Mazhar S. Finger-vein recognition using a novel enhancement method with convolutional neural network. J Chin Inst Eng. 2021;44(5):407–17.

    Article  Google Scholar 

  11. Bozkurt F. A study on CNN based transfer learning for recognition of flower species. Avrupa Bilim Ve Teknol Dergisi. 2022;32:883–90.

    Google Scholar 

  12. Chung CL, Huang KJ, Chen SY, Lai MH, Chen YC, Kuo YF. Detecting Bakanae disease in rice seedlings by machine vision. Comput Electron Agric. 2016;121:404–11.

    Article  Google Scholar 

  13. Eser SERT. A deep learning based approach for the detection of diseases in pepper and potato leaves. Anadolu Tarım Bilimleri Dergisi. 2021;36(2):167–78.

    Google Scholar 

  14. Esgario JG, Krohling RA, Ventura JA. Deep learning for classification and severity estimation of coffee leaf biotic stress. Comput Electron Agric. 2020;169: 105162.

    Article  Google Scholar 

  15. Ferentinos KP. Deep learning models for plant disease detection and diagnosis. Comput Electron Agric. 2018;145:311–8.

    Article  Google Scholar 

  16. Ganganagowder NV, Kamath P. Intelligent classification models for food products basis on morphological, colour and texture features. Acta Agron. 2017;66(4):486–94.

    Article  Google Scholar 

  17. Gerdan D, Caner KOÇ, Vatandaş M. Diagnosis of tomato plant diseases using pre-trained architectures and a proposed convolutional neural network model. J Agric Sci. 2023;29(2):618–29.

    Google Scholar 

  18. Guo Y, Zhang J, Yin C, Hu X, Zou Y, Xue Z, Wang W. Plant disease identification based on deep learning algorithm in smart farming. Discr Dyn Nat Soc. 2020;2020:1–11.

    MATH  Google Scholar 

  19. Hughes D, Salathé M. An open access repository of images on plant health to enable the development of mobile disease diagnostics; 2015. ar**v preprint ar**v:1511.08060

  20. Liang Q, **ang S, Hu Y, Coppola G, Zhang D, Sun W. PD2SE-Net: computer-assisted plant disease diagnosis and severity estimation network. Comput Electron Agric. 2019;157:518–29.

    Article  Google Scholar 

  21. Mohanty SP, Hughes DP, Salathé M. Using deep learning for image-based plant disease detection. Front Plant Sci. 2016;7:1010.

    Article  Google Scholar 

  22. Mukherjee J, Mitra SK. Enhancement of color images by scaling the DCT coefficients. IEEE Trans Image Process. 2008;17(10):1783–94.

    Article  MathSciNet  MATH  Google Scholar 

  23. Nagaraju M, Chawla P. Systematic review of deep learning techniques in plant disease detection. Int J Syst Assur Eng Manage. 2020;11(3):547–60.

    Article  Google Scholar 

  24. Pal T, Jaiswal V, Chauhan RS. DRPPP: A machine learning based tool for prediction of disease resistance proteins in plants. Comput Biol Med. 2016;78:42–8. https://doi.org/10.1016/j.compbiomed.2016.09.008.

    Article  Google Scholar 

  25. Pantazi XE, Moshou D, Tamouridou AA. Automated leaf disease detection in different crop species through image features analysis and one class classifiers. Comput Electron Agric. 2019;156:96–104.

    Article  Google Scholar 

  26. Rahman SU, Alam F, Ahmad N, Arshad S. Image processing based system for the detection, identification and treatment of tomato leaf diseases. Multimed Tools Appl. 2023;82(6):9431–45.

    Article  Google Scholar 

  27. Raza SA, Prince G, Clarkson JP, Rajpoot NM. Automatic detection of diseased tomato plants using thermal and stereo visible light images. PLoS ONE. 2015;10: e0123262. https://doi.org/10.1371/journal.pone.0123262.

    Article  Google Scholar 

  28. Rumpf T, Mahlein A-K, Steiner U, Oerke E-C, Dehne H-W, Plümer L. Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Comput Electron Agric. 2010;74:91–9.

    Article  Google Scholar 

  29. Sabrol H, Kumar S. Intensity based feature extraction for tomato plant disease recognition by classification using decision tree. Int J Comput Sci Inf Secur (IJCSIS). 2016;14(9):622–6.

    Google Scholar 

  30. Saleem MH, Potgieter J, Arif KM. Plant disease classification: a comparative evaluation of convolutional neural networks and deep learning optimizers. Plants. 2020;9(10):1319.

    Article  Google Scholar 

  31. Singh CB, Jayasa DS, Paliwala J, White NDG. Identification of insect-damaged wheat kernels using short-wave near-infrared hyperspectral and digital colour imaging. Comput Electron Agric. 2010;73:118–25.

    Article  Google Scholar 

  32. Singh D, Yadav DK, Fatima F. Characterization and genetic diversity of Pantoea agglomerans isolates having dual potentiality to suppress growth of Ralstonia solanacearum and plant growth promoting ability. Indian Phytopathol. 2020;73:643–53. https://doi.org/10.1007/s42360-020-00268-1.

    Article  Google Scholar 

  33. Singh V, Misra AK. Detection of plant leaf diseases using image segmentation and soft computing techniques. Inf Process Agric. 2017;4(1):41–9. https://doi.org/10.1016/j.inpa.2016.10.005.

    Article  Google Scholar 

  34. Verma S, Chug A, Singh AP. Application of convolutional neural networks for evaluation of disease severity in tomato plant. J Discr Math Sci Cryptogr. 2020;23(1):273–82.

    Google Scholar 

  35. Wang G, Sun Y, Wang J. Automatic image-based plant disease severity estimation using deep learning. Hindawi Comput Intell Neurosci. 2017;2017:1–8.

    Google Scholar 

  36. Yigit E, Sabanci K, Toktas A, Kayabasi A. A study on visual features of leaves in plant identification using artificial intelligence techniques. Comput Electron Agric. 2019;156:369–77.

    Article  Google Scholar 

  37. Zamani AS, Anand L, Rane KP, Prabhu P, Buttar AM, Pallathadka H, et al. Performance of machine learning and image processing in plant leaf disease detection. J Food Qual. 2022;2022:1–7.

    Article  Google Scholar 

Download references

Acknowledgements

This study was supported by the Department of Science and Technology (DST), Government of India, New Delhi, under the ICPS Programme (Project Tilted “Application of Internet of Things (IoT) in Agriculture Sector”, Reference No. T-319).

Author information

Authors and Affiliations

  1. University School of Information, Communication and Technology (USIC&T), Guru Gobind Singh Indraprastha University (GGSIPU), New Delhi, India

    Shradha Verma, Anuradha Chug & Amit Prakash Singh

  2. Division of Plant Pathology, Indian Agricultural Research Institute (IARI), New Delhi, India

    Dinesh Singh

Authors
  1. Shradha Verma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anuradha Chug
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amit Prakash Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dinesh Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shradha Verma.

Ethics declarations

Conflict of Interest

The authors declare no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Verma, S., Chug, A., Singh, A.P. et al. Plant Disease Detection and Severity Assessment Using Image Processing and Deep Learning Techniques. SN COMPUT. SCI. 5, 83 (2024). https://doi.org/10.1007/s42979-023-02417-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-023-02417-5

Keywords

Search

Navigation