VGG16 for Plant Image Classification with Transfer Learning and Data Augmentation

  • Authors

    • Mohamad Aqib Haqmi Abas
    • Nurlaila Ismail
    • Ahmad Ihsan Mohd Yassin
    • Mohd Nasir Taib
    2018-10-02
    https://doi.org/10.14419/ijet.v7i4.11.20781
  • convolutional neural network, transfer learning, dropout, data augmentation, deep learning.

  • Abstract

    This paper discusses the potential of applying VGG16 model architecture for plant classification. Flower images are used instead of leaves as in other plant recognition model because the structure of shape and color of leaves are similar in nature. This might be disadvantageous when we want to use only leaves images as a sole feature of plants to classify the species. Previous work has demonstrated the effectiveness of using transfer learning, dropout and data augmentation as a method to reduce overfitting problem of convolutional neural network model when applied in limited amount of images data. We have successfully build and train the VGG16 model with 2800 flower images. The model able to achieve a classification accuracy score of 96.25% for training set, 93.93% for validation set and 89.96% for testing set.

     

     

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  • How to Cite

    Aqib Haqmi Abas, M., Ismail, N., Ihsan Mohd Yassin, A., & Nasir Taib, M. (2018). VGG16 for Plant Image Classification with Transfer Learning and Data Augmentation. International Journal of Engineering & Technology, 7(4.11), 90-94. https://doi.org/10.14419/ijet.v7i4.11.20781

    Received date: 2018-10-02

    Accepted date: 2018-10-02

    Published date: 2018-10-02