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Proceedings Paper

Statistical methods for texture analysis applied to agronomical images
Author(s): F. Cointault; L. Journaux; P. Gouton
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Paper Abstract

For activities of agronomical research institute, the land experimentations are essential and provide relevant information on crops such as disease rate, yield components, weed rate... Generally accurate, they are manually done and present numerous drawbacks, such as penibility, notably for wheat ear counting. In this case, the use of color and/or texture image processing to estimate the number of ears per square metre can be an improvement. Then, different image segmentation techniques based on feature extraction have been tested using textural information with first and higher order statistical methods. The Run Length method gives the best results closed to manual countings with an average error of 3%. Nevertheless, a fine justification of hypothesis made on the values of the classification and description parameters is necessary, especially for the number of classes and the size of analysis windows, through the estimation of a cluster validity index. The first results show that the mean number of classes in wheat image is of 11, which proves that our choice of 3 is not well adapted. To complete these results, we are currently analysing each of the class previously extracted to gather together all the classes characterizing the ears.

Paper Details

Date Published: 26 February 2008
PDF: 9 pages
Proc. SPIE 6813, Image Processing: Machine Vision Applications, 68130W (26 February 2008); doi: 10.1117/12.768649
Show Author Affiliations
F. Cointault, ENESAD-UP (France)
L. Journaux, Faculté Universitaire des Sciences Agronomiques de Gembloux (Belgium)
P. Gouton, Univ. of Burgundy (France)

Published in SPIE Proceedings Vol. 6813:
Image Processing: Machine Vision Applications
Kurt S. Niel; David Fofi, Editor(s)

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