PROCEEDINGS ARTICLE | February 26, 2008
Proc. SPIE. 6813, Image Processing: Machine Vision Applications
KEYWORDS: Statistical analysis, Visualization, Image segmentation, Image processing, Feature extraction, Light sources and illumination, Image classification, Ear, Statistical methods, Algorithm development
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.
