Developing an Index for Detection and Identification of Disease Stages

Ashourloo, D; Matkan, AA; Huete, A; Aghighi, H; Mobasheri, MR

Developing an Index for Detection and Identification of Disease Stages

Ashourloo, D Matkan, AA Huete, A

Aghighi, H Mobasheri, MR

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Geoscience and Remote Sensing Letters, 2016, 13, (6), pp. 851-855
Issue Date:: 2016-06-01

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Field	Value	Language
dc.contributor.author	Ashourloo, D
dc.contributor.author	Matkan, AA
dc.contributor.author	Huete, A https://orcid.org/0000-0003-2809-2376
dc.contributor.author	Aghighi, H
dc.contributor.author	Mobasheri, MR
dc.date.accessioned	2022-03-18T04:08:23Z
dc.date.available	2022-03-18T04:08:23Z
dc.date.issued	2016-06-01
dc.identifier.citation	IEEE Geoscience and Remote Sensing Letters, 2016, 13, (6), pp. 851-855
dc.identifier.issn	1545-598X
dc.identifier.issn	1558-0571
dc.identifier.uri	http://hdl.handle.net/10453/155330
dc.description.abstract	Spectral data have been widely used to estimate the disease severity (DS) levels of different plants. However, such data have not been evaluated to estimate the disease stages of the plant. This study aimed at developing a spectral disease index (SDI) that is able to identify the stages of wheat leaf rust disease at various DS levels. To meet the aim of the study, the reflectance spectra (350-2500 nm) of infected leaves with different symptom fractions and DS levels were measured with a spectroradiometer. Then, pure spectra of the different disease symptoms at the leaf scale were analyzed, and a new function was developed to find the wavelengths most sensitive to disease symptom fraction. The reflectance spectra with highest sensitivity were found at 675 and 775 nm. Finally, the normalized difference of DS and the ratio ρ675/ρ775 was used as a new SDI to discriminate three different levels of the disease stage at the canopy level. The suggested SDI showed a promising performance to improve the detection disease stages in precision plant protection.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Geoscience and Remote Sensing Letters
dc.relation.isbasedon	10.1109/LGRS.2016.2550529
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 0909 Geomatic Engineering
dc.subject.classification	Geological & Geomatics Engineering
dc.title	Developing an Index for Detection and Identification of Disease Stages
dc.type	Journal Article
utslib.citation.volume	13
utslib.for	0909 Geomatic Engineering
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
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
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	closed_access	*
dc.date.updated	2022-03-18T04:08:22Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	13
utslib.citation.issue	6

Abstract:

Spectral data have been widely used to estimate the disease severity (DS) levels of different plants. However, such data have not been evaluated to estimate the disease stages of the plant. This study aimed at developing a spectral disease index (SDI) that is able to identify the stages of wheat leaf rust disease at various DS levels. To meet the aim of the study, the reflectance spectra (350-2500 nm) of infected leaves with different symptom fractions and DS levels were measured with a spectroradiometer. Then, pure spectra of the different disease symptoms at the leaf scale were analyzed, and a new function was developed to find the wavelengths most sensitive to disease symptom fraction. The reflectance spectra with highest sensitivity were found at 675 and 775 nm. Finally, the normalized difference of DS and the ratio ρ675/ρ775 was used as a new SDI to discriminate three different levels of the disease stage at the canopy level. The suggested SDI showed a promising performance to improve the detection disease stages in precision plant protection.

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http://hdl.handle.net/10453/155330