Plant recognition system based on extreme learning machine by using shearlet transform and new geometric features

Abstract

To date, different approaches have been used to be correctly identified of plant species. Leaves are the most important approaches as part of the plants which provide many features with advantages such as shape, color and vein texture. In this study, a new approach based on the geometrical properties of the leaf has been proposed. This method called Edge Step (ES), consists of features such as angle, center-edge length and edge distance by using edge points in the shape boundary curve. In addition, Shearlet Transform method, which has features such as good sensitivity to tissue identification, rapid calculation and directional independence, is used. In addition to these methods, Color features and Gray-Level Co-Occurrence Matrix (GLCM) method to extract color and texture properties from leaf images have been applied. Attributes derived from all these methods were tested with the Extreme Learning Machine (ELM) classifier method as separately and combination. The proposed study has been tested by using four different plant leaf datasets such as Flavia, Swedish, ICL and Foliage. Using these datasets, studies based on texture, shape and color characteristics have been compared with the performance of the proposed approach. As a result, the proposed method is identified to be more successful than the other methods.