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.

Classifying Indian Medicinal Leaf Species Using LCFN-BRNN Model

“…We select time sequence features based on the firing mechanism and the grouping of white pixels. The previously used algorithm [1][2][3][4] in leaf species classification extracts the shape, vein, tip, base and margin features separately. It is an extremely tedious process to establish, approximately, the venation pattern or leaf base.…”

“…To resolve the problems above faced by pharmacologists and practitioners of Ayurveda, Unani and Siddha with regard to herbal plants, an efficient plant species classification tool using computer vision techniques is the need of the hour. Arun et al (2017) [3] proposed an optimized color channel and texture models to classify medicinal leaves native to Kanyakumari district in the state of Tamil Nadu in India. Only five species of medicinal leaves were considered, totalling 250 images in all.…”

“…Further, separate normalization and dimension reduction techniques are not called for. Image features are easily extracted using the LCFN, without their being aligned [2][3][4][5].…”

Color space and color channel selection on image segmentation of food images

“…Image comparison between original image with HSL converted imageYUVThe YUV model consists of a luminance/brightness component (Y) and two-color/chrominance content components (U and V). Based on references U of YUV color channel in image segmentation achieved above 90% when identifying medical plant leaf[20]. The formula for converting RGB to YUV is displayed in Equation 11.= 0.299 × + 0.587 × + 0.114 × = 0.493( − )…”

Evaluation of Color Models for Palm Oil Fresh Fruit Bunch Ripeness Classification

“…However, ANN takes a long processing time due to over-fitting issue. Research on color model evaluation have been done for medical plant identifier using texture features with different classifiers namely Stochastic Gradient Descent (SGD), k-nearest neighbour (kNN), Support vector machine (SVM), Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) classifiers [41]. SVM produces the best identification result compared to the other classifiers.…”