Texture or Color Analysis in Agronomic Images for Wheat Ear Counting

Abstract

In agronomy, image processing techniques are more and more used to detect crop, weeds, diseases ... We proposed to study the feasibility to use color and/or texture analysis to evaluate the number of wheat ears per m2 to simplify the manual countings currently done. In this paper we present firstly the use of color and texture image processing together to detect the ears, before to propose and compare different texture image segmentation techniques based on feature extraction by first and higher order statistical methods. The extracted features are used for unsupervised pixel classification to obtain the different classes in the image, before to use the k-means algorithm. Three methods have been tested with very heterogeneous results, except the run length technique for which the results are close to the manual countings (66% error). The hypothesis took into account for the textural analysis methods are currently modify to justify them more accurately, especially concerning the number of classes and the size of the analysis window.