Identifying Medicinal Plant Leaves using Textures and Optimal Colour Spaces Channel

Abstract

This paper presents an automated medicinal plant leaf identification system. The Colour Texture analysis of the leaves is done using the statistical, the Grey Tone Spatial Dependency Matrix(GTSDM) and the Local Binary Pattern(LBP) based features with 20 different colour spaces(RGB, XYZ, CMY, YIQ, YUV, $YC_{b}C_{r}$, YES, $U^{*}V^{*}W^{*}$, $L^{*}a^{*}b^{*}$, $L^{*}u^{*}v^{*}$, lms, $l\alpha\beta$, $I_{1} I_{2} I_{3}$, HSV, HSI, IHLS, IHS, TSL, LSLM and KLT). Classification of the medicinal plant is carried out with 70\% of the dataset in training set and 30\% in the test set. The classification performance is analysed with Stochastic Gradient Descent(SGD), kNearest Neighbour(kNN), Support Vector Machines based on Radial basis function kernel(SVM-RBF), Linear Discriminant Analysis(LDA) and Quadratic Discriminant Analysis(QDA) classifiers. Results of classification on a dataset of 250 leaf images belonging to five different species of plants show the identification rate of 98.7 \%. The results certainly show better identification due to the use of YUV, $L^{*}a^{*}b^{*}$ and HSV colour spaces.

Keywords

• colour spaces, texture features, plant identification, pattern recognition