Identification of Commonly used Medicinal Leaves using Machine Learning Techniques with SIFT Corner Detector as Features

Abstract

Medicinal leaves carry a huge value and importance in the medical field which can be directly used or medicines are made for medicinal purposes to cure patients. With the variety of leaves present, the proper identification of the leaves is very difficult without prior knowledge and experience. Computer Vision can bring the accurate identification of such leaves using the various feature extraction techniques using leaf images. The aim is to build a methodology using various feature extraction techniques to extract features, clustering algorithm to cluster the features and decision trees as a classifier. Feature extraction techniques like SIFT key descriptors which are robust and provide matching in spite of the change in intensity, size or rotation of the object in the images. Effective corner points are chosen from the image from which magnitude and orientation of surrounding are used to build descriptor that is the vector of feature for each corner points. For clustering the data, various partitional, hierarchical, density based methods are used to cluster the data which cluster the data with respect to inter-connectivity, similarity, closeness, etc. The clusters data is used to build the decision tree like C4.5 and CART which uses entropy and Gini index as the splitting criteria. All these methodologies put together to form an effective method to efficiently recognize the unknown leaf image using trained model.