Weed seeds identification based on structure elements' descriptor

Abstract

The implementation of new methods for automatic, reliable identification and classification of seeds is of great technical and economic importance in agricultural industry. As in ocular inspection, the automatic classification of seeds should be based on knowledge of seed size, shape, color and texture. In this work, we assess the discriminating power of these characteristics for the unique identification of seeds of 216 weed species. We identified a nearly optimal set of 4 (three morphological and one color and textural) seed characteristics as classification parameters, using the performance of the Support Vector Machines as classifier. Among these characteristics, color and textural features are extracted and described by SED (structure elements' descriptor) simultaneously which proves to perform better than other image retrieval methods. The main findings of this paper are shown in the strong discrimination power of SED. Moreover, experimental results suggest that recognition rate reaches the peak with the combination of the morphological characteristics and SED.