Change search
ReferencesLink to record
Permanent link

Direct link
Hyperspectral crop reflectance data for characterising and estimating fungal disease severity in wheat
Uppsala Univ, Ctr Image Anal, .ORCID iD: 0000-0002-1831-9285
2005 (English)In: Biosystems Engineering, ISSN 1537-5110, E-ISSN 1537-5129, Vol. 91, no 1, 9-20 p.Article in journal (Refereed) Published
Abstract [en]

Many studies have shown the usefulness of hyperspectral crop reflectance data for detecting plant pathological stress. However, there is still a need to identify unique signatures for specific stresses amidst the constantly changing background associated with normal crop growth and development. Comparing spatial and temporal patterns in crop spectra can provide such signatures. This work was concerned with characterising and estimating fungal disease severity in a spring wheat crop. This goal can be accomplished by using a reference data set consisting of hyperspectral crop reflectance data vectors and the corresponding disease severity field assessments. The hyperspectral vectors were first normalised into zero-mean and unit-variance vectors by performing various combinations of spectral- and band-wise normalisations. Then, after applying the same normalisation procedures to the new hyperspectral data, a nearest-neighbour classifier was used to classify the new data against the reference data. Finally, the corresponding stress signatures were computed using a linear transformation model. High correlation was obtained between the classification results and the corresponding field assessments of fungal disease severity, confirming the usefulness and efficiency of this approach. The effects of increased disease severity could be characterised by analysing the resulting disease signatures obtained when applying the different normalisation procedures. The low computational load of this approach makes it suitable for real-time on-vehicle applications.

Place, publisher, year, edition, pages
2005. Vol. 91, no 1, 9-20 p.
Keyword [en]
spectral-biophysical data, multisite analyses, plant stress, optical-parameters, leaf reflectance, neural-networks, soybean leaves, citrus leaves, ozone damage, vegetation
National Category
Agricultural Sciences
URN: urn:nbn:se:kth:diva-14781DOI: 10.1016/j.biosystemseng.2005.02.007ISI: 000229394500002OAI: diva2:332822

QC 20100525

Available from: 2010-08-05 Created: 2010-08-05 Last updated: 2014-02-04Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Hamid Muhammed, Hamed
In the same journal
Biosystems Engineering
Agricultural Sciences

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 41 hits
ReferencesLink to record
Permanent link

Direct link