Detection of sclerotinia rot disease on celery using hyperspectral data and partial least squares regression

Huang, J-F. and Apan, Armando ORCID: https://orcid.org/0000-0002-5412-8881 (2006) Detection of sclerotinia rot disease on celery using hyperspectral data and partial least squares regression. Journal of Spatial Science, 51 (2). pp. 129-142. ISSN 1449-8596

Preview

PDF 005_Huang_Revised.pdf Download (437kB)

Abstract

There is a need to detect and assess the incidence of Sclerotinia rot disease in celery (Apium graveolens). In this study, we examined the potential of hyperspectral sensing to detect the symptoms of this disease in celery crop. Using a portable spectrometer, sample measurements of diseased and healthy leaves were collected from celery leaves in the field. Both raw and transformed spectral data were used in the development of Partial Least Squares regression models. The cross-validated results showed that the incidence of disease on celery could be predicted using the raw spectra and the first and second derivative data, with prediction errors ranging from 11.08 to 13.62%. The visible and near-infrared wavelengths (400-1300nm) produced similar detection ability with that of the full range wavelengths (400-2500nm).

Statistics for this ePrint Item

Item Type:	Article (Commonwealth Reporting Category C)
Refereed:	Yes
Item Status:	Live Archive
Additional Information:	http://www.mappingsciences.org.au/journal.htm
Faculty/School / Institute/Centre:	Historic - Faculty of Engineering and Surveying - Department of Surveying and Land Information (Up to 30 Jun 2013)
Faculty/School / Institute/Centre:	Historic - Faculty of Engineering and Surveying - Department of Surveying and Land Information (Up to 30 Jun 2013)
Date Deposited:	11 Oct 2007 00:53
Last Modified:	02 Jul 2013 22:40
Uncontrolled Keywords:	hyperspectral sensing, disease, Sclerotinia
Fields of Research (2008):	09 Engineering > 0909 Geomatic Engineering > 090905 Photogrammetry and Remote Sensing
URI:	http://eprints.usq.edu.au/id/eprint/1874

Actions (login required)

Archive Repository Staff Only