Statistical methods for texture analysis applied to agronomical images

F. Cointault; L. Journaux; P. Gouton

doi:10.1117/12.768649

26 February 2008 Statistical methods for texture analysis applied to agronomical images

F. Cointault, L. Journaux, P. Gouton

Proceedings Volume 6813, Image Processing: Machine Vision Applications; 68130W (2008) https://doi.org/10.1117/12.768649
Event: Electronic Imaging, 2008, San Jose, California, United States

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.

Citation Download Citation

F. Cointault, L. Journaux, and P. Gouton "Statistical methods for texture analysis applied to agronomical images", Proc. SPIE 6813, Image Processing: Machine Vision Applications, 68130W (26 February 2008); https://doi.org/10.1117/12.768649

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