PLANT LEAF RECOGNITION SYSTEM USING KERNEL ENSEMBLE APPROACH

Abstract

The information about the classification of plant leaf into appropriate taxonomies is very useful for botanists. In this study, an efficient Plant Leaf Recognition (PLR) system is designed using kernel ensemble approach by Support Vector Machine (SVM). At first, the plant leaf images are normalized and resized by color normalization and bicubic interpolation. Features such as 4^th order color moments and nine energy maps of LAWS are combined to form a feature database. The classification is done by ensemble approach with different SVM kernels like Linear (SVM-L), Radial basis function (SVM-R), Polynomial (SVM-P) and Quadratic (SVM-Q). Finally, the outputs of each SVM classifier are fused to classify plant leaf images. The PLR system is carried on using Folio database that contains 640 leaf images captured from 32 species. The system achieves 90.63% recognition rate by the ensemble approach using colour moments and texture features by LAWS.