A Combined Color, Texture and Edge Features Based Approach for Identification and Classification of Indian Medicinal Plants

Abstract

This paper presents a method for identification and classification of images of medicinal plants such as herbs, shrubs and trees based on color and texture feature using SVM and neural network classifier. The tribal people in India classify plants according to their medicinal values. In the system of medicine called Ayurveda, identification of medicinal plants is considered an important activity in the preparation of herbal medicines. Ayurveda medicines have become alternate for allopathic medicine. Hence, leveraging technology in automatic identification and classification of medicinal plants has become essential. Plant species belonging to different classes such as Papaya, Neem, Tulasi, Aloe and Garlic are considered in this work. This paper presents edge and color descriptors that have low-dimension, effective and simple. In addition, the rotation invariant texture descriptors namely, directional difference and the gradient histogram are used. These features are obtained from 900 images of medicinal plants and used to train and test the image samples of three classes with SVM and radial basis exact fit neural network (RBENN). The classification accuracies for color, edge texture features are 74% and 80% respectively. The accuracy is improved to 90% with combined color and texture features. The results are encouraging for tree image plants than herbs and shrubs due to distinguishing feature of stem.