Maize leaf disease classification using deep convolutional neural networks

  • Ramar Ahila PriyadharshiniEmail author
  • Selvaraj Arivazhagan
  • Madakannu Arun
  • Annamalai Mirnalini
Original Article


Crop diseases are a major threat to food security. Identifying the diseases rapidly is still a difficult task in many parts of the world due to the lack of the necessary infrastructure. The accurate identification of crop diseases is highly desired in the field of agricultural information. In this study, we propose a deep convolutional neural network (CNN)-based architecture (modified LeNet) for maize leaf disease classification. The experimentation is carried out using maize leaf images from the PlantVillage dataset. The proposed CNNs are trained to identify four different classes (three diseases and one healthy class). The learned model achieves an accuracy of 97.89%. The simulation results for the classification of maize leaf disease show the potential efficiency of the proposed method.


Deep learning CNN Maize leaf disease PCA whitening 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Zhang Z, He X, Sun X, Guo L, Wang J, Wang F (2015) Image recognition of maize leaf disease based on GA-SVM. Chem Eng Trans 46:199–204Google Scholar
  2. 2.
    Alehegn E (2017) Maize leaf diseases recognition and classification based on imaging and machine learning techniques. Int J Innov Res Comput Commun Eng 5(12):1–11Google Scholar
  3. 3.
    Ren J (2012) ANN vs. SVM: which one performs better in classification of MCCs in mammogram imaging. Knowl Based Syst 26:144–153CrossRefGoogle Scholar
  4. 4.
    Jafari I, Masihi M, Zarandi MN (2018) Scaling of counter-current imbibition recovery curves using artificial neural networks. J Geophys Eng 15(3):1062–1070CrossRefGoogle Scholar
  5. 5.
    LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324CrossRefGoogle Scholar
  6. 6.
    Badea MS, Felea II, Florea LM, Vertan C (2016) The use of deep learning in image segmentation, classification and detection. arXiv:1605.09612 [cs.CV]
  7. 7.
    Xu X, Dehghani A, Corrigan D, Caulfield S, Moloney D (2016), Convolutional neural network for 3D object recognition using volumetric representation. In: First international workshop on sensing, processing and learning for intelligent machines (SPLINE)Google Scholar
  8. 8.
    Brahimi M, Boukhalfa K, Moussaoui A (2016) Deep learning of tomato diseases: classification and symptoms visualization. Appl Artif Intell. Google Scholar
  9. 9.
    Yang L, Yi S, Zebg N, Liu Y, Zhang Y (2017) Identification of rice diseases using deep convolutional neural networks. Neurocomputing 267:378–384CrossRefGoogle Scholar
  10. 10.
    Kawaski R, Uga H, Kagiwada S, Iyatomi H (2015) Basic study of viral plant diseases using convolutional neural networks. In: Proceedings of the international symposium on visual computing, pp 638–645Google Scholar
  11. 11.
    dos Santos Ferreria A, Freitas DM, da Silva GG (2017) Weed detection in soybean crops using ConvNets. Comput Electron Agric 143:314–324CrossRefGoogle Scholar
  12. 12.
  13. 13.
  14. 14.
    Li F-F, Johnson J, Yeung S (2017) Convolutional neural networks for visual recognition lecture notesGoogle Scholar
  15. 15.
    Zhang L, Yang B (2014) Research on recognition of maize disease based on mobile internet and support vector machine technique. Adv Mater Res 905:659–662. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of ECEMepco Schlenk Engineering CollegeSivakasiIndia

Personalised recommendations