Automatic seeds recognition by size, form and texture features

This work deals with an automatic seeds analysis system based on pattern recognition methods. However, this paper emphasizes only the pattern recognition aspects of the problem and, for our tests, four hundred samples of each of four species of seeds, namely corn, oat, barley and lentil are considered. The recognition procedure is, firstly, made on the basis of shape features and texture features, separately. Both of theses methods have given good results with a small confusion rate. In order to increase the recognition rate, the shape and texture features are used all together leading then to results considerably improved. After images acquisition and their pre-processing, the general process includes the features space reduction using the Principal Component and clustering operation based the k-means algorithm. The decision phase is based on the nearest Euclidean distance rule between the feature vector of an unknown seed and the average feature vector of each cluster.