Early Detection of Sugarcane Smut Disease in Hyperspectral Images
View/ Open
File version
Accepted Manuscript (AM)
Author(s)
Bao, Dong
Zhou, Jun
Bhuiyan, Shamsul Arafin
Zia, Ali
Ford, Rebecca
Gao, Yongsheng
Griffith University Author(s)
Year published
2021
Metadata
Show full item recordAbstract
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 ...
View more >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.
View less >
View more >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.
View less >
Conference Title
2021 36th International Conference on Image and Vision Computing New Zealand (IVCNZ)
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