Plants play a crucial role in the lives of all living things. A risk of extinction exists for many plants, hence many botanists and scientists are working in order to protect plants and plant diversity. Plant identification is the most important part of studies carried out for this purpose. In order to identify plants more accurately, different approaches have been used in the studies to date. One of these approaches is plant identification through leaf recognition, and is the basis of many conducted studies. It can be used for automatic plant recognition in the area of botany, the food sector, industry, medicine, and in many more areas too. In this study, image processing based on feature extraction methods such as color features, vein features, Fourier Descriptors (FD), and Gray-Level Co-occurrence Matrix (GLCM) methods are used. This study suggests the use of features extracted from leaves divided into two or four parts, instead of extracting for the whole leaf. Both the individual and combined performances of each feature extraction method are calculated by Extreme Learning Machines (ELM) classifier. The suggested approach has been applied to the Flavia leaf dataset. 10-fold cross-validation was used to evaluate the accuracy of the proposed method, which was then compared and tabulated with methods from other studies. The evaluated accuracy of the proposed method on the Flavia leaf dataset was calculated as 99.10%. (C) 2019 Elsevier Inc. All rights reserved.