Identification of medicinal plant by fuzzy local binary pattem and multi objective genetic algorithm

Abstract

This research proposes multi-objective genetic algorithm non-dominated-sorting (MOGA NSGA-II) of fuzzy local binary pattern to optimize LBP operator and fuzzy threshold for identification of Indonesian medicinal plants. Multi-objective genetic algorithm (MOGA) is the genetic algorithm (GA) which is developed specifically for problems with multiple objectives. We evaluated 1,440 medicinal plant leaf images which belong to 30 species. The images were taken from Biofarmaka IPB, Cikabayan Farm, Greenhouse Center Ex-Situ Conservation of Medicinal Plant Indonesia Tropical Forest and Gunung Leutik. FLBP is used to handle uncertainty on images with various patterns. FLBP approach is based on the assumption that a local image neighbourhood may be characterized by more than a single binary pattern. The experimental results show that the correct selection of FLBP operator and threshold using MOGA can reach accuracy of 85%. It can be concluded that this propose method is capable to identify medicinal plants species efficiently and accurately.