Artificial Intelligence-Based Plant’s Diseases Classification

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


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.


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


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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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