@inbook{44d4b34ca9654c8c8a45877b4616883f,
title = "UAV Multispectral Remote Sensing for Yellow Rust Mapping: Opportunities and Challenges",
abstract = "Wheat is threatened by various crop stresses in its life-cycle, where yellow rust is a severe disease significantly impacting wheat yield. This work aims to investigate the use of Unmanned Aerial Vehicle based multispectral remote sensing for winter wheat stress mapping caused by yellow rust disease. A simple unsupervised wheat yellow rust mapping framework is initially proposed by integrating Spectral Vegetation Indices generation, mutual information analysis and Otsu{\textquoteright}s thresholding. A field experiment is carefully designed by infecting winter wheat with different levels of yellow rust inoculum, where UAV multispectral images are collected at the diseased stage with visible symptoms. Experimental results on the labelled dataset initially show the effectiveness of the proposed unsupervised framework for yellow rust disease mapping. Limitations of the proposed algorithm and challenges of yellow rust detection for real-life applications are also discussed.",
keywords = "Precision agriculture, Remote sensing, Unmanned Aerial Vehicle (UAV), Unsupervised learning",
author = "Jinya Su and Cunjia Liu and Chen, \{Wen Hua\}",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.",
year = "2022",
month = may,
doi = "10.1007/978-981-19-2027-1\_7",
language = "English",
isbn = "978-981-19-2026-4",
volume = "2",
series = "Smart Agriculture (Singapore)",
publisher = "Springer",
pages = "107--122",
booktitle = "Smart Agriculture (Singapore)",
}