AUTOMATED CLASSIFICATION OF HAIR CARE PLANTS USING GEOMETRICAL AND TEXTURAL FEATURES FROM LEAF IMAGES: A PATTERN RECOGNITION BASED APPROACH

Abstract

Automated classification plays a vital role in content based image retrieval systems
in addition to many more. Inter-class similarity and intra-class dissimilarity is the main challenge
posed by leaf classification. This research work proposed a plant classification system using textural
and geometrical features from leaf images. Six classification models, among which three were
ensemble methods, were considered to evaluate the accuracy of proposed technique. Train and test
strategy was adopted to evaluate the performance of different classifiers. Experimental results showed
that the proposed technique outperformed the state of the art. Moreover, it was observed that textural
features outperformed geometrical features. The best accuracy achieved with textural features was
100%, whereas it was 98.8% when geometrical features were used. SVM, IBk and Random Tree
remained the best classifiers in leaf identification using both types of features. Hence, textural and
geometrical features could be effectively used for plant classification