Comparison of Segmentation Techniques Used for Classification of Ayurvedic Medicinal Leaf

Abstract: This paper introduces a method for segmentation of images of medicinal plants. 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. Ayurvedic 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 Ajwain, Betal, Curry, Methi, Milkweed, Neem and Tulsi are considered in this work.
Clustering is an unsupervised technique is used for organizing the data for efficient retrieval. This is mainly used in pattern reorganization and data analysis. Today many cluster analysis techniques are used for data analysis and have proven to be very useful in segmentation. Performance of these algorithms is data dependent. In this paper K-Means and Fuzzy C-Means are implemented for segmenting the Ayurvedic medicinal leaf. The proposed research work compares the computing performance and clustering accuracy of K-Means clustering with FCM clustering algorithm. Experimental results showed that higher performance is achieved by K-Means clustering when compared with FCM.

Keywords: Medicinal Plants leaf, Image processing, color Segmentation, Classification