Medicinal Leaves Classification Using Random Forest and AdaBoost

The identification of medicinal plants is crucial for both biodiversity preservation and the creation of potent medications. Machine learning techniques will be employed to effectively classify and differentiate medicinal plant leaves based on their unique characteristics, enabling accurate species identification. The leaf images from six different species namely Neem, Tulasi, Peepal, Mint, Basale, Fenugreek have been collected. A comprehensive set of features, including shape features and texture features will be computed from the preprocessed images. In this project, the application of machine learning algorithms such as Random Forest and AdaBoost, for the classification of medicinal plant leaves will be being analyzed. The Random Forest classifies the images with and accuracy of 93.15% and the AdaBoost with an accuracy of 84.93%. Through the integration of some algorithms, this study seeks to provide a comprehensive framework for effectively categorizing different medicinal plant species based on their distinct features.