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  • Early Detection of Sugarcane Smut Disease in Hyperspectral Images

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    Bao528398-Accepted.pdf (4.165Mb)
    File version
    Accepted Manuscript (AM)
    Author(s)
    Bao, Dong
    Zhou, Jun
    Bhuiyan, Shamsul Arafin
    Zia, Ali
    Ford, Rebecca
    Gao, Yongsheng
    Griffith University Author(s)
    Bhuiyan, Shamsul
    Bao, Dong
    Zhou, Jun
    Zia, Ali
    Ford, Ruth
    Gao, Yongsheng
    Ford, Rebecca
    Year published
    2021
    Metadata
    Show full item record
    Abstract
    Sugarcane smut, caused by the fungus Sporisorium scitamineum, is a serious sugarcane disease in Queensland, which can cause 30-100% production loss. Early detection of smut disease is a key step towards disease management. However, early-stage smut symptoms are not visible to the human eye. To address this challenge, we leverage the capability of hyperspectral imaging in data acquisition beyond the human visual spectrum and propose a deep Convolutional Neural Network (CNN) to classify sugarcane images as infected with S. scitamineum or healthy. A key component of the CNN is the Dual Self-Attention Block (DSAB) module that ...
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    Sugarcane smut, caused by the fungus Sporisorium scitamineum, is a serious sugarcane disease in Queensland, which can cause 30-100% production loss. Early detection of smut disease is a key step towards disease management. However, early-stage smut symptoms are not visible to the human eye. To address this challenge, we leverage the capability of hyperspectral imaging in data acquisition beyond the human visual spectrum and propose a deep Convolutional Neural Network (CNN) to classify sugarcane images as infected with S. scitamineum or healthy. A key component of the CNN is the Dual Self-Attention Block (DSAB) module that is proposed to identify important image features both spectrally and spatially. Experiments on a collected hyperspectral image dataset show the effectiveness of our proposed method in detecting smut disease before visible symptoms appear.
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    Conference Title
    2021 36th International Conference on Image and Vision Computing New Zealand (IVCNZ)
    DOI
    https://doi.org/10.1109/ivcnz54163.2021.9653386
    Copyright Statement
    © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subject
    Image processing
    Agriculture, land and farm management
    Publication URI
    http://hdl.handle.net/10072/411699
    Collection
    • Conference outputs

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