Skip to main content
SpringerLink
Log in

AI Based Automated Model for Plant Disease Detection, a Deep Learning Approach

  • Conference paper
  • First Online:
Computational Intelligence in Communications and Business Analytics (CICBA 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1406))

Abstract

Plants can be affected by different diseases and many of them are expressed through leaves. Plant diseases are major issues in the field of agriculture and automatic detection of diseases can reduce production costs. In the present study, an automated disease detection model from leaf images has been proposed which is based on Convolutional Neural Network. Five diseases, which use leaves as one of their expression mediums, have been considered in this study. These are Early Blight, Late Blight, Esca, Isariopsis and Black Rot. The first two are mostly found in Potatoes and the rest three are in Grapes. Images of healthy and symptom expressive leaves have been taken for each species and the proposed model has been trained and tested using them. Overall efficiency of our proposed model is found to be 87.47% for Potatoes and 91.96% for Grapes. The results have been analyzed from different aspects in various scales. The efficiency of our model has also been measured against some of the existing models.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. Kimberly, L., Kelly, J., Smith, R., Gauthier, N.: Plant diseases (2016)

    Google Scholar 

  2. Kaur, S., Pandey, S., Goel, S.: Plants disease identification and classification through leaf images: a survey. Arch. Comput. Methods Eng. 26(2), 507–530 (2018). https://doi.org/10.1007/s11831-018-9255-6

    Article  Google Scholar 

  3. Waals, J.E.: A review of early blight of potato. Afr. Plant Prot. 7, 91–102 (2001)

    Google Scholar 

  4. Arora, R., Sharma, S., Singh, B.: Late blight disease of potato and its management. Potato J. 41, 16–40 (2014)

    Google Scholar 

  5. Mugnai, L., Graniti, A., Surico, G.: Esca (black measles) and brown wood-streaking: two old and elusive diseases of grapevines. Plant Dis. 83, 404–418 (1999)

    Article  Google Scholar 

  6. Mehmet, F.: Occurrence of isariopsis leaf spot or blight of vitis rupestris caused by pseudocercospora vitis in Turkey (2017)

    Google Scholar 

  7. Jermini, M., Gessler, C.: Epidemiology and control of grape black rot in southern Switzerland. Plant Dis. 80, 322–325 (1996)

    Article  Google Scholar 

  8. Fang, Y., Ramasamy, R.: Current and prospective methods for plant disease detection. Biosensors 5, 537–561 (2015)

    Article  Google Scholar 

  9. Yamashita, R., Nishio, M., Do, R., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights Imaging 9, 06 (2018)

    Article  Google Scholar 

  10. Boulent, J., Foucher, S., Théau, J., St-Charles, P.-L.: Convolutional neural networks for the automatic identification of plant diseases. Front. Plant Sci. 10, 941 (2019)

    Article  Google Scholar 

  11. Harte, E.: Plant disease detection using CNN. Ph.D. thesis, September 2020

    Google Scholar 

  12. Huang, K.-Y.: Application of artificial neural network for detecting phalaenopsis seedling diseases using color and texture features. Comput. Electron. Agric. 57(1), 3–11 (2007)

    Article  Google Scholar 

  13. Shrivastava, S., Hooda, D.S.: Automatic brown spot and frog eye detection from the image captured in the field. Am. J. Intell. Syst. 4(4), 131–134 (2014)

    Google Scholar 

  14. Rothe, P.R., Kshirsagar, R.V.: Cotton leaf disease identification using pattern recognition techniques. In: 2015 International Conference on Pervasive Computing (ICPC), pp. 1–6 (2015)

    Google Scholar 

  15. Sabrol, H., Kumar, S.: Intensity based feature extraction for tomato plant disease recognition by classification using decision tree. Int. J. Comput. Sci. Inf. Secur. 14, 622–626 (2016)

    Google Scholar 

  16. Anthonys, G., Wickramarachchi, N.: An image recognition system for crop disease identification of paddy fields in Sri Lanka, December 2009

    Google Scholar 

  17. Ferdouse Ahmed Foysal, Md., Shakirul Islam, M., Abujar, S., Akhter Hossain, S.: A novel approach for tomato diseases classification based on deep convolutional neural networks. In: Uddin, M.S., Bansal, J.C. (eds.) Proceedings of International Joint Conference on Computational Intelligence. AIS, pp. 583–591. Springer, Singapore (2020). https://doi.org/10.1007/978-981-13-7564-4_49

  18. Hussain, M., Bird, J., Faria, D.: A study on CNN transfer learning for image classification, June 2018

    Google Scholar 

  19. Agarap, A.F.: Deep learning using rectified linear units (ReLU), March 2018

    Google Scholar 

  20. Wu, S.G., Bao, F.S., Xu, E.Y., Wang, Y.-X., Chang, Y.-F., Xiang, Q.-L. : A leaf recognition algorithm for plant classification using probabilistic neural network (2007)

    Google Scholar 

  21. Mohanty, S.P., Hughes, D.P., Salathé, M.: Using deep learning for image-based plant disease detection. Front. Plant Sci. 7, 1419 (2016)

    Article  Google Scholar 

  22. Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006). ROC Analysis in Pattern Recognition

    Article  MathSciNet  Google Scholar 

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

    Google Scholar 

  24. Meunkaewjinda, A., Kumsawat, P., Attakitmongcol, K., Srikaew, A.: Grape leaf disease detection from color imagery using hybrid intelligent system. In: 2008 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, vol. 1, pp. 513–516 (2008)

    Google Scholar 

  25. Padol, P.B., Yadav, A.A.: SVM classifier based grape leaf disease detection. In: 2016 Conference on Advances in Signal Processing (CASP), pp. 175–179 (2016)

    Google Scholar 

  26. Kole, D.K., Ghosh, A., Mitra, S.: Detection of downy mildew disease present in the grape leaves based on fuzzy set theory. In: Kumar Kundu, M., Mohapatra, D.P., Konar, A., Chakraborty, A. (eds.) Advanced Computing, Networking and Informatics- Volume 1. SIST, vol. 27, pp. 377–384. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07353-8_44

    Chapter  Google Scholar 

  27. Xu, G., Zhang, F., Shah, S., Ye, Y., Mao, H.: Use of leaf color images to identify nitrogen and potassium deficient tomatoes. Pattern Recogn. Lett. 32, 1584–1590 (2011)

    Article  Google Scholar 

  28. Majumdar, D., Kole, D., Chakraborty, A., Majumder, D.: An integrated digital image analysis system for detection, recognition and diagnosis of disease in wheat leaves, August 2015

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The Department of Master of Computer Applications, Techno India Hooghly, Chinsurah, 712101, West Bengal, India

    Aditi Ghosh

  2. The Department of Computer Science, The University of Burdwan, Purba Bardhaman, 713104, West Bengal, India

    Parthajit Roy

Authors
  1. Aditi Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Parthajit Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Visva-Bharati University, Santiniketan, India

    Paramartha Dutta

  2. University of Kalyani, Kalyani, India

    Jyotsna K. Mandal

  3. Assam University, Silchar, India

    Somnath Mukhopadhyay

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ghosh, A., Roy, P. (2021). AI Based Automated Model for Plant Disease Detection, a Deep Learning Approach. In: Dutta, P., Mandal, J.K., Mukhopadhyay, S. (eds) Computational Intelligence in Communications and Business Analytics. CICBA 2021. Communications in Computer and Information Science, vol 1406. Springer, Cham. https://doi.org/10.1007/978-3-030-75529-4_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-75529-4_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-75528-7

  • Online ISBN: 978-3-030-75529-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation