@inproceedings{615d14eca5014b1a977bbebcf7cc1671,
title = "Early Detection of Sugarcane Smut Disease in Hyperspectral Images",
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 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.",
keywords = "Deep Learning, Hyperspectral Imaging, Self-attention, Smut Disease Detection, Sugarcane",
author = "Dong Bao and Jun Zhou and Bhuiyan, {Shamsul Arafin} and Ali Zia and Rebecca Ford and Yongsheng Gao",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 36th International Conference on Image and Vision Computing New Zealand, IVCNZ 2021 ; Conference date: 09-12-2021 Through 10-12-2021",
year = "2021",
doi = "10.1109/IVCNZ54163.2021.9653386",
language = "English",
series = "International Conference Image and Vision Computing New Zealand",
publisher = "IEEE Computer Society",
editor = "Cree, {Michael J.}",
booktitle = "Proceedings of the 2021 36th International Conference on Image and Vision Computing New Zealand, IVCNZ 2021",
address = "United States",
}