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.
Author: C H Arun, D
Christopher Durairaj
Journal Code: jptkomputergg170004
