• Login
    View Item 
    •   UZ eScholar Home
    • Faculty of Science
    • Faculty of Science ETDs
    • Faculty of Science e-Theses Collection
    • View Item
    •   UZ eScholar Home
    • Faculty of Science
    • Faculty of Science ETDs
    • Faculty of Science e-Theses Collection
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Deep learning for improved plant classification

    Thumbnail
    View/Open
    Kwangware_Deep_Learning_for_Improved_Plant_Classification.pdf (2.351Mb)
    Date
    2018
    Author
    Kwangware, Innocent T.
    Metadata
    Show full item record

    Abstract
    Plants are crucial in the ecosystem: they enable life by providing oxygen, they have medicinal properties, among many other uses, and hence their classification is vital. Automating plant identification has remained a challenging task. Machine learning does not automate feature engineering, making its application to the problem an arduous task. Deep learning automates featuring engineering, but requires large datasets, presenting another challenge. This research proposes an improved and fast plant classification model for plants using deep learning on small datasets. The objectives of this research are to explore techniques that are state-of-the-art in the classification of plants using leaves, to improve the classification accuracy scores, and to model a framework for the classification. A convolutional neural network was designed and applied on a small leaf dataset with fine-tuning. An accuracy score of 94.99% was achieved. High overfitting was noted since the dataset was small. Data augmentation was applied to the dataset with the images augmented before being input into the model, differing from the many cases where augmentation is applied on-the-fly. This increased the success rate to 99.99% with reduced overfitting. This showed that augmentation applied in this way improves the performance of the model. Transfer learning with the same augmentation method was applied, resulting in a 100% test accuracy and stable results with low overfitting. The proposed methodology provided good results on the Flavia leaf data set used in the experiments. Finally, a deep learning framework for improved plant classification using leaves is outlined.
    URI
    http://hdl.handle.net/10646/3884
    Additional Citation Information
    Kwangware I.T. (2018). Deep learning for improved plant classification. (Unpublished thesis). University of Zimbabwe.
    Subject
    Machine learning
    Deep learning
    plant classification model
    convolutional neural network
    Transfer learning
    overfitting
    feature engineering
    feature extraction
    Collections
    • Faculty of Science e-Theses Collection [257]

    University of Zimbabwe: Educating To Change Lives!
    DSpace software copyright © 2002-2020  DuraSpace | Contact Us | Send Feedback
     

     

    Browse

    All of UZ eScholarCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister

    Statistics

    View Usage StatisticsView Google Analytics Statistics

    University of Zimbabwe: Educating To Change Lives!
    DSpace software copyright © 2002-2020  DuraSpace | Contact Us | Send Feedback