Evaluating the effect of different wheat rust disease symptoms on vegetation indices using hyperspectral measurements

Ashourloo, D; Mobasheri, MR; Huete, A

Evaluating the effect of different wheat rust disease symptoms on vegetation indices using hyperspectral measurements

Ashourloo, D Mobasheri, MR Huete, A

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Publication Type:: Journal Article
Citation:: Remote Sensing, 2014, 6 (6), pp. 5107 - 5123
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Ashourloo, D	en_US
dc.contributor.author	Mobasheri, MR	en_US
dc.contributor.author	Huete, A https://orcid.org/0000-0003-2809-2376	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Remote Sensing, 2014, 6 (6), pp. 5107 - 5123	en_US
dc.identifier.uri	http://hdl.handle.net/10453/35723
dc.description.abstract	© 2014 by the authors. Spectral Vegetation Indices (SVIs) have been widely used to indirectly detect plant diseases. The aim of this research is to evaluate the effect of different disease symptoms on SVIs and introduce suitable SVIs to detect rust disease. Wheat leaf rust is one of the prevalent diseases and has different symptoms including yellow, orange, dark brown, and dry areas. The reflectance spectrum data for healthy and infected leaves were collected using a spectroradiometer in the 450 to 1000 nm range. The ratio of the disease-affected area to the total leaf area and the proportion of each disease symptoms were obtained using RGB digital images. As the disease severity increases, so does the scattering of all SVI values. The indices were categorized into three groups based on their accuracies in disease detection. A few SVIs showed an accuracy of more than 60% in classification. In the first group, NBNDVI, NDVI, PRI, GI, and RVSI showed the highest amount of classification accuracy. The second and third groups showed classification accuracies of about 20% and 40% respectively. Results show that few indices have the ability to indirectly detect plant disease.	en_US
dc.relation.ispartof	Remote Sensing	en_US
dc.relation.isbasedon	10.3390/rs6065107	en_US
dc.title	Evaluating the effect of different wheat rust disease symptoms on vegetation indices using hyperspectral measurements	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.for	060705 Plant Physiology	en_US
utslib.for	0203 Classical Physics	en_US
utslib.for	0406 Physical Geography and Environmental Geoscience	en_US
utslib.for	0909 Geomatic Engineering	en_US
pubs.embargo.period	Not known	en_US
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
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	open_access
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	6	en_US

Abstract:

© 2014 by the authors. Spectral Vegetation Indices (SVIs) have been widely used to indirectly detect plant diseases. The aim of this research is to evaluate the effect of different disease symptoms on SVIs and introduce suitable SVIs to detect rust disease. Wheat leaf rust is one of the prevalent diseases and has different symptoms including yellow, orange, dark brown, and dry areas. The reflectance spectrum data for healthy and infected leaves were collected using a spectroradiometer in the 450 to 1000 nm range. The ratio of the disease-affected area to the total leaf area and the proportion of each disease symptoms were obtained using RGB digital images. As the disease severity increases, so does the scattering of all SVI values. The indices were categorized into three groups based on their accuracies in disease detection. A few SVIs showed an accuracy of more than 60% in classification. In the first group, NBNDVI, NDVI, PRI, GI, and RVSI showed the highest amount of classification accuracy. The second and third groups showed classification accuracies of about 20% and 40% respectively. Results show that few indices have the ability to indirectly detect plant disease.

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http://hdl.handle.net/10453/35723