Skip to main content
SpringerLink
Log in

Mango leaf disease recognition using neural network and support vector machine

  • Original Article
  • Published:
Iran Journal of Computer Science Aims and scope Submit manuscript

Abstract

The mango tree is affected by different diseases and it is very difficult to detect disease in naked eye. This paper presents a neural network ensemble (NNE) for mango leaf disease recognition (MLDR) that help to identify diseases easily and correctly instead of traditional system. This study intends to detect the diseases of mango leaf with machine learning monitoring different symptoms of leaves. Here, trained data are produced by classification technique collecting images of leaves that were various disease affected. A machine learning system is designed to identify the symptom of mangoes’ leaf diseases automatically uploading and matching new images of affected leaf with trained data. The proposed system could successfully detect and classify the examined disease with average accuracy of 80%. This proposed solution would clinch the mango plants. The system will help to detect disease without the presence of agriculturist that would save time to identify disease with machine instead of manual system. It would also ease to treat the affected mango leaf disease properly, increase the production of mango, and meet the demand of global market.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  1. Al Bashish, D., Braik, M., Bani-Ahmad, S.: Detection and classification of leaf diseases using k-means-based segmentation and. Inf. Technol. J. 10(2), 267–275 (2011)

    Article  Google Scholar 

  2. Anand R., V.S., Aravinth, J.: An application of image processing techniques for detection of diseases on brinjal leaves using k-means clustering method. In: IEEE international conference on circuit, power and computing technologies (ICCPCT). IEEE (2016)

  3. Arivazhagan, S., Shebiah, R.N., Ananthi, S., Varthini, S.V.: Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agric. Eng. Int. CIGR J. 15, 211–217 (2013)

    Google Scholar 

  4. Bhange, M., Hingoliwala, H.: Smart farming: pomegranate disease detection using image processing. Proced. Comput. Sci. 58, 280–288 (2015)

    Article  Google Scholar 

  5. Bourbos, V., Skoudridakis, M., et al.: First report of Oidium mangiferae on Mangifera indica in Greece. Plant Dis. 79(10), 1075 (1995)

    Google Scholar 

  6. Dai, Q.Y., Zhang, Cp, Wu, H.: Research of decision tree classification algorithm in data mining. Int. J. Database Theory Appl. 9(5), 1–8 (2016)

    Article  Google Scholar 

  7. Francis, J., Anto Sahaya Dhas, D., Anoop, B.: Identification of leaf diseases in pepper plants using soft computing techniques. In: Conference on emerging devices and smart systems (ICEDSS), pp. 168–173 (2016)

  8. Kajale, R.R.: Detection & reorganization of plant leaf diseases using image processing and Android OS. Int. J. Eng. Res. Gen. Sci. 3(2), (2015)

  9. Kaur, S., Pandey, S., Goel, S.: Plants disease identification and classification through leaf images: a survey. Arch. Comput. Methods Eng. 26(2), 507–530 (2019)

    Article  Google Scholar 

  10. Kishun, R.: Loss in mango fruit due to bacterial canker xanthomonas Mangiferae indicae. In: Proceedings of the fifth international conference on plant pathogenic bacteria, August 16–23, 1981 at CIAT, Cali, Colombia/technical editor J. Carlos Lozano; production editor Paul Gwin. [Cali, Colombia]: Centro Internacional de Agricultura Tropical (1982)

  11. Krishnan, M., Sumithra, M.: A novel algorithm for detecting bacterial leaf scorch (BLS) of shade trees using image processing. In: IEEE 11th Malaysia international conference on communications. IEEE (2013)

  12. Kulkarni, H., Patil Ashwin, K.: Leaf disease classification using artificial neural networks and decision tree classifier. J. Image Process. Pattern Recognit. Prog. 5, (2018)

  13. Kumar, J., Singh, U., Beniwal, S.: Mango malformation: one hundred years of research. Annu. Rev. Phytopathol. 31(1), 217–232 (1993)

    Article  Google Scholar 

  14. Madiwalar, C., Wyawahare, V.: Plant disease identification: a comparative study. In: International conference on data management, analytics and innovation (ICDMAI) pp. 13–18 (2017)

  15. Peterson, R., Schipke, L., Clarkson, P.: Significance of two mango flower diseases in the dry tropics. In: III International mango symposium Vol. 291, pp. 338–345 (1989)

  16. Prakash, O., Misra, A., Raoof, M.: Studies on mango bacterial canker disease. Bio. Mem. 20, 95–107 (1994)

    Google Scholar 

  17. Ranjan, M., Weginwar, R., Neha, J., Ingole, A.: Detection and classification of leaf disease using artificial neural network. Int. J. Tech. Res. Appl. 3(3), 331–333 (2015)

    Google Scholar 

  18. 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. 74(1), 91–99 (2010)

    Article  Google Scholar 

  19. Samajpati, B.J., Degadwala, S.D.: Hybrid approach for apple fruit diseases detection and classification using random forest classifier. In: 2016 International conference on communication and signal processing (ICCSP), pp. 1015–1019. IEEE (2016)

  20. Sharma, R., Singh, V.P.: A research on automatic detection of defects in mango fruit through image processing and machine learning techniques. J. Adv. Sch. Res. Allied Educ. 12, 1258–1268 (2017)

    Google Scholar 

  21. Shergill, D., Rana, A., Singh, H.: Extraction of rice disease using image processing. Int. J. Eng. Sci. Res. Technol. (IJESRT) 4(6), 135–143 (2015)

    Google Scholar 

  22. Smith, J.: Bark cracking in mango trees. Citrus Subtrop. Fruit Grow. 479, (1973)

  23. Sohi, H., Sokhi, S., Tiwari, R.: Studies on the storage rot of mango caused by colletotrichum gloeosporioides penz. and its control/studi sul marciume dei frutti conservati di mango da colletotrichum gloeosporioides penz. e sui relativi mezzi di lotta. Phytopathologia Mediterranea pp. 114–116 (1973)

  24. Singh, U.P., Singh, S., Chouhan, S.J., Jain, S.: Multilayer convolution neural network for the classification of mango leaves infected by anthracnose disease. IEEE Access 7, 43721–43729 (2019)

  25. Tlhobogang, B., Wannous, M.: Design of plant disease detection system: a transfer learning approach work in progress. In: 2018 IEEE International conference on applied system invention (ICASI), pp. 158–161. IEEE (2018)

  26. Ullagaddi, S., Raju, S.V.: Disease recognition in mango crop using modified rotational kernel transform features. In: 2017 4th International conference on advanced computing and communication systems (ICACCS), pp. 1–8. IEEE (2017)

  27. Warne, P.P., Ganorkar, S.: Detection of diseases on cotton leaves using k-mean clustering method. Int. Res. J. Eng. Technol. (IRJET) 2(4), 425–431 (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of CSE, Gono Bishwabidyalay, Dhaka, Bangladesh

    Md. Rasel Mia, Sujit Roy & Md. Atikur Rahman

  2. Department of CSE, Jatiya Kabi Kazi Nazrul Islam University, Dhaka, Bangladesh

    Sujit Roy & Subrata Kumar Das

Authors
  1. Md. Rasel Mia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sujit Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Subrata Kumar Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Md. Atikur Rahman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Subrata Kumar Das.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mia, M.R., Roy, S., Das, S.K. et al. Mango leaf disease recognition using neural network and support vector machine. Iran J Comput Sci 3, 185–193 (2020). https://doi.org/10.1007/s42044-020-00057-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42044-020-00057-z

Keywords

Search

Navigation