By Topic

Using hyperspectral reflectance data for discrimination between healthy and diseased plants, and determination of damage-level in diseased plants

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Muhammed, H.H. ; Centre for Image Anal., Uppsala Univ., Sweden

It has been found, through many research works, that hyperspectral reflectance data can be used for studying the pathological conditions of crops. The influence of the pathological status of a crop on its spectral characteristics can be visible or detectable in the visible and/or the near-infrared regions of the electromagnetic spectrum, depending on the spectral effects of the pathological conditions of the crop. Differences in the spectral characteristics between normal (i.e. healthy) crops and others suffering from physiological stress or disease, can be revealed and/or magnified by simply normalising the data properly. Such effects can be achieved by normalising the hyperspectral reflectance data into zero-mean and unit variance vectors (i.e. whitening the data). Spectral-wise and/or band-wise normalisation can be performed here. In the experimental part of this work we used a reference data set consisting of hyperspectral reflectance data vectors and the corresponding field measurements of leaf-damage level in the plants. Then, after normalising the new hyperspectral reflectance data; a nearest neighbour classifier is used to classify our new data against the reference data. The correlation coefficient and the sum of squared differences are used as distance measures (between two vectors) in the nearest neighbour classifier. High correlation is obtained between the classification results and the corresponding field leaf-damage measurements, confirming the usefulness and efficiency of this method for this type of analysis.

Published in:

Applied Imagery Pattern Recognition Workshop, 2002. Proceedings. 31st

Date of Conference:

16-17 Oct. 2002