Quantitative identification of yellow rust in winter wheat with a new spectral index: Development and validation using simulated and experimental data

Ren, Y; Huang, W; Ye, H; Zhou, X; Ma, H; Dong, Y; Shi, Y; Geng, Y; Huang, Y; Jiao, Q; Xie, Q

Quantitative identification of yellow rust in winter wheat with a new spectral index: Development and validation using simulated and experimental data

Ren, Y Huang, W Ye, H Zhou, X Ma, H Dong, Y Shi, Y Geng, Y Huang, Y Jiao, Q Xie, Q

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: International Journal of Applied Earth Observation and Geoinformation, 2021, 102, pp. 1-11
Issue Date:: 2021-10-01

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Field	Value	Language
dc.contributor.author	Ren, Y
dc.contributor.author	Huang, W
dc.contributor.author	Ye, H
dc.contributor.author	Zhou, X
dc.contributor.author	Ma, H
dc.contributor.author	Dong, Y
dc.contributor.author	Shi, Y
dc.contributor.author	Geng, Y
dc.contributor.author	Huang, Y
dc.contributor.author	Jiao, Q
dc.contributor.author	Xie, Q https://orcid.org/0000-0002-1576-6610
dc.date.accessioned	2022-02-14T03:29:03Z
dc.date.available	2022-02-14T03:29:03Z
dc.date.issued	2021-10-01
dc.identifier.citation	International Journal of Applied Earth Observation and Geoinformation, 2021, 102, pp. 1-11
dc.identifier.issn	0303-2434
dc.identifier.issn	1872-826X
dc.identifier.uri	http://hdl.handle.net/10453/154489
dc.description.abstract	Yellow rust, caused by Puccinia striiformis f. sp. Tritici, is a serious disease attacking wheat (Triticum aestivum L.) across the globe. The occurrence of yellow rust can result in severe yield reduction and economic loss. Hyperspectral remote sensing has demonstrated potential in detecting yellow rust, with the majority of studies distinguishing qualitatively between diseased and healthy individuals or performing simple grading of disease severity. However, research on the quantification of the severity of yellow rust is limited. To fill this gap in the literature, in the current study, we constructed a new spectral index, the yellow rust optimal index (YROI), using the hyperspectral data obtained by ASD field spectrometer to quantitatively estimate yellow rust severity. The index is based on the spectral response of spores, and vegetation biophysical and biochemical parameters (VPCPs); and integrated with the PROSPECT-D model. We evaluated the new index and compared it with 11 commonly used yellow rust detection indices using experimental leaf- and canopy-scale spectral datasets. Results demonstrated the superior accuracy of YROI for both the leaf (R2 = 0.822, RMSE = 0.070) and canopy (R2 = 0.542, RMSE = 0.085) scales. In this research, we quantitatively analyzed the spectral response mechanism of wheat yellow rust, which provided a new idea for the quantitative identification of crop diseases. Moreover, our results can be employed as a reference and theoretical basis for the accurate and timely quantitative identification of crop diseases over the large areas in the future.
dc.language	English
dc.publisher	Elsevier
dc.relation	University of Technology Sydney
dc.relation.ispartof	International Journal of Applied Earth Observation and Geoinformation
dc.relation.isbasedon	10.1016/j.jag.2021.102384
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0406 Physical Geography and Environmental Geoscience, 0909 Geomatic Engineering
dc.subject.classification	Geological & Geomatics Engineering
dc.title	Quantitative identification of yellow rust in winter wheat with a new spectral index: Development and validation using simulated and experimental data
dc.type	Journal Article
utslib.citation.volume	102
utslib.for	0406 Physical Geography and Environmental Geoscience
utslib.for	0909 Geomatic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2022-02-14T03:28:59Z
pubs.publication-status	Published
pubs.volume	102

Abstract:

Yellow rust, caused by Puccinia striiformis f. sp. Tritici, is a serious disease attacking wheat (Triticum aestivum L.) across the globe. The occurrence of yellow rust can result in severe yield reduction and economic loss. Hyperspectral remote sensing has demonstrated potential in detecting yellow rust, with the majority of studies distinguishing qualitatively between diseased and healthy individuals or performing simple grading of disease severity. However, research on the quantification of the severity of yellow rust is limited. To fill this gap in the literature, in the current study, we constructed a new spectral index, the yellow rust optimal index (YROI), using the hyperspectral data obtained by ASD field spectrometer to quantitatively estimate yellow rust severity. The index is based on the spectral response of spores, and vegetation biophysical and biochemical parameters (VPCPs); and integrated with the PROSPECT-D model. We evaluated the new index and compared it with 11 commonly used yellow rust detection indices using experimental leaf- and canopy-scale spectral datasets. Results demonstrated the superior accuracy of YROI for both the leaf (R2 = 0.822, RMSE = 0.070) and canopy (R2 = 0.542, RMSE = 0.085) scales. In this research, we quantitatively analyzed the spectral response mechanism of wheat yellow rust, which provided a new idea for the quantitative identification of crop diseases. Moreover, our results can be employed as a reference and theoretical basis for the accurate and timely quantitative identification of crop diseases over the large areas in the future.

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http://hdl.handle.net/10453/154489