Skip to main content

Automatic Recognition of Plant Leaf Diseases Using Deep Learning (Multilayer CNN) and Image Processing

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


Bangladesh is highly dependent on Agricultural production on it’s economic strength, however different types of diseases have made an enormous damage on the growth of agricultural crops. Bacterial Blight and leaf brown spot (common rust) are the most common diseases affecting the guava, mango, rice, corn and peach plant. Thus, it is become very essential to detect leaf diseases early to protect from damaging the entire crop. The farmers have no enough knowledge about the leaf disease and they used manual process in order to identify disorder. So, the detection accuracy is not good enough and time consuming. An automated and accurate identification system has become essential to overcome this problem. In this paper, a novel technique to diagnose and classify guava, mango, rice, corn and peach diseases has been proposed. One of the effective and modern method for finding the disorder and providing appropriate treatment is deep learning. We have mainly focused on CNN algorithm for training the dataset and found 95.26% accuracy rate. Identification of the diseases would help Bangladesh to grow its economy as it will increase the production rate of guava, mango, rice, corn and peach plants.

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

Access this chapter

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

Similar content being viewed by others


  1. Tariqul Islam, M., Tusher, A.N.: Automatic detection of grape, potato and strawberry leaf diseases using CNN and ımage processing. In: Nanda, P., Verma, V.K., Srivastava, S., Gupta, R.K., Mazumdar, A.P. (eds.) Data Engineering for Smart Systems. Lecture Notes in Networks and Systems, vol. 238. Springer, Singapore (2022).

  2. Gaikwad, S.S.: Identification of fungi infected leaf diseases using deep learning techniques. Turk. J. Comput. Math. Educ. 12(6), 5618–5625 (2021)

    Google Scholar 

  3. Trang, K., TonThat, L., Gia Minh Thao, N. Tran Ta Thi, N.: Mango diseases ıdentification by a deep residual network with contrast enhancement and transfer learning. In: 2019 IEEE Conference on Sustainable Utilization and Development in Engineering and Technologies (CSUDET), pp. 138–142 (2019).

  4. Ramesh, S., Vydeki, D.: Rice blast disease detection and classification using machine learning algorithm. In: 2018 2nd International Conference on Micro-Electronics and Telecommunication Engineering (ICMETE), pp. 255–259 (2018).

  5. Ayub, U., Moqurrab, S.A.: Predicting crop diseases using data mining approaches: classification. In: 2018 1st International Conference on Power, Energy and SmartGrid (Icpesg).

  6. Kambale1, G., Bilgi, N.: A Survey Paper On Crop Disease Identification And Classification Using Pattern Recognition And Digital Image Processing Techniques. May (2017)

    Google Scholar 

  7. Prem Rishi Kranth, G., Hema Lalitha, M., Basava, L., Mathur, A.: Plant disease prediction using machine learning algorithms. Int. J. Comput. Appl. 182(25) (2018)

    Google Scholar 

  8. Jyoti, J.B., Tanuja, S.Z.: Cotton plant leaf diseases identification using support vector machine. Int. J. Recent Sci. Res. 8(12), 22395–22398 (2017)

    Google Scholar 

  9. Kanabur, V., Harakannanavar, S.S., Purnikmath, V.I., Hullole, P., Torse, D.: Detection of leaf disease using hybrid feature extraction techniques and CNN classifier. In: International Conference on Computational Vision and Bio Inspired Computing, pp. 1213–1220. Springer, Cham (2019)

    Google Scholar 

  10. Akila, M., Deepan, P.: Detection and classificationof plant leaf diseases by using deep learning algorithm. In: International Journal Of Engineering Research & Technology (Ijert) Issn: 2278-0181 Published By, www.Ijert.Org Iconnect—2k18 Conference Proceedings (2018)

    Google Scholar 

  11. Deb, S., Islam, S.M.R., RobaiatMou, J., Islam, M.T.: Design and implementation of low cost ECG monitoring system for the patient using smart device. In: 2017 International Conference on Electrical, Computer and Communication Engineering (ECCE), pp. 774–778). IEEE, February (2017)

    Google Scholar 

  12. Islam, M.T., Islam, S.M.R.: A new ımage quality ındex and it’s application on MRI image (2021)

    Google Scholar 

  13. Sungheetha, A., Rajesh Sharma, R.: Classification of remote sensing image scenes using double feature extraction hybrid deep learning approach. J. Inf. Technol. 3(02), 133–149 (2021)

    Google Scholar 

  14. Karuppusamy, P.: Building detection using two-layered novel convolutional neural networks. J. Soft Comput. Paradigm 3(01), 29–37 (2021)

    Article  Google Scholar 

  15. Islam, S.M.R., Islam, M.T., Huang, X.: A new approach of image quality index. In: 2017 4th International Conference on Advances in Electrical Engineering (ICAEE), pp. 223–228 (2017).

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Abdur Nur Tusher .

Editor information

Editors and Affiliations

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

Tusher, A.N., Islam, M.T., Sammy, M.S.R., Hasna, S.A., Chakraborty, N.R. (2022). Automatic Recognition of Plant Leaf Diseases Using Deep Learning (Multilayer CNN) and Image Processing. 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.

Download citation

Publish with us

Policies and ethics