Combination of morphological, Local Binary Pattern Variance and color moments features for Indonesian medicinal plants identification

We propose a new method for Indonesian medicinal plants identification using combination of some leaf features, i.e. texture, shape, and color. Local Binary Pattern Variance (LBPV) is used to extract leaf texture, morphological feature is used to extract leaf shape, and color moment is used to extract leaf color distribution. In the experiment we used 51 species of Indonesian medicinal plants and each species consists of 48 images, so the total images used in this research are 2,448 images. Combination of leaf feature is done using Product Decision Rule (PDR) and classification of medicinal plants is done using Probabilistic Neural Network (PNN). The experimental results show that the combination of the morphological, LBPV, and color moments features can improve the accuracy of medicinal plants identification. This research is important to enhance utilization of Indonesian medicinal plants.