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Artificial Intelligence-Based Plant’s Diseases Classification

  • Lobna M. Abou El-MagedEmail author
  • Ashraf Darwish
  • Aboul Ella Hassanien
Conference paper
  • 180 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1153)

Abstract

In this paper, we proposed an artificial intelligence model for plants diseases classification based on convolutional neural network (CNN). The proposed model consists of three phases; (a) preprocessing phase, which augmented the data and balanced the dataset; (b) classification and evaluation phase based on pre-train CNN VGG16 and evaluate the results; (c) optimize the hyperparameters of CNN using Gaussian method. The proposed model is tested on the plant’s images dataset. The dataset consists of nine plants with thirty-three cases for diseased and healthy plant’s leaves. The experimental results before the optimization of pre-trained CNN VGG16 achieve 95.87% classification accuracy. The experimental results improved to 98.67% classification accuracy after applied the Gaussian process for optimizing hyperparameters.

Keywords

Plants diseases Deep learning Convolution neural network Hyper parameter optimization Gaussian process 

References

  1. 1.
    Ferentinos, K.P.: Deep learning models for plant disease detection and diagnosis. Comput. Electron. Agric. 145, 311–318 (2018)CrossRefGoogle Scholar
  2. 2.
    Zhang, X., et al.: Identification of maize leaf diseases using improved deep convolutional neural networks. In: IEEE Xplore Digital Library, Digital Object Identifier (2018).  https://doi.org/10.1109/access.2018.2844405
  3. 3.
    Barbedo, J.G.A.: Factors influencing the use of deep learning for plant disease recognition. Biosyst. Eng. 72, 84–91 (2018)CrossRefGoogle Scholar
  4. 4.
    The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches. https://arxiv.org/abs/1803.01164
  5. 5.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Proceedings of 25th International Conference on Neural Information Processing Systems, Lake Tahoe, Nevada, 03–06 December 2012, vol. 1, pp. 1097–1105 (2012)Google Scholar
  6. 6.
    Sermanet, P. et al.: Overfeat: integrated recognition, localization and detection using convolutional networks. arXiv:1312.6229 (2013)
  7. 7.
    Krizhevsky, A.: One weird trick for parallelizing convolutional neural networks. arXiv:1404.5997 (2014)
  8. 8.
    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)Google Scholar
  9. 9.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014)
  10. 10.
  11. 11.
    LeCun, Y., et al.: Gradient based learning applied to document recognition. In: Proceedings of the IEEE, November 1998Google Scholar
  12. 12.
    Wang, T., Li, B.: Data dropout: optimizing training data for convolutional neural networks. arXiv:1809.00193v2 (2018)
  13. 13.
    Islam, M.S. et al.: InceptB: a CNN based classification approach for recognizing traditional bengali games. In: 8th International Conference on Advances in Computing and Communication (ICACC-2018). Elsevier (2018)Google Scholar
  14. 14.
    Feurer, A., Hutter, F.: Automated machine learning: methods, systems, challenges. In: Chapter1: Hyperparameter Optimization. Springer (2019)Google Scholar
  15. 15.
  16. 16.
    Hussain, M., Bird, J.J., Faria, D.R.: Advances in Computational Intelligence Systems. In: UKCI 2018, AISC 840, pp. 191–202 (2019)Google Scholar
  17. 17.
    Rasmussen, C.E., Williams, C.K.I.: Gaussian processes for machine learning. The MIT Press (2006). www.GaussianProcess.org/gpml. ISBN 026218253X. c, Massachusetts Institute of Technology
  18. 18.

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Lobna M. Abou El-Maged
    • 1
    • 4
    Email author
  • Ashraf Darwish
    • 2
    • 4
  • Aboul Ella Hassanien
    • 3
    • 4
  1. 1.Computer Science DepartmentMET High InstituteMansouraEgypt
  2. 2.Faculty of ScienceHelwan UniversityCairoEgypt
  3. 3.Faculty of Computers and InformationCairo UniversityGizaEgypt
  4. 4.Scientific Research Group in Egypt (SRGE)CairoEgypt

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