Medicinal and Aromatic Plants Identification Using Machine Learning Methods

Abstract

In this study, different machine learning (ML) methods were used to classify medicinal and aromatic plants (MAP) namely St. John’s wort (Hypericum perforatum L.), Melissa (Melissa officinalis L.), Echinacea (Echinacea purpurea L.), Thyme (Thymus sp.) and Mint (Mentha angustifolia L.) based on leaf shape, gray and fractal features. Naive Bayes Classifier (NBC), Classification and Regression Tree (CART), K-Nearest Neighbor (KNN), and Probabilistic Neural Network (PNN) classification were used as methods. The results indicated that plant species were successfully recognized the average of correct classification rate. The best classification rate on the NBC was taken: training data for classification rate 98.39% and test data classification rate for 98.00% are obtained. ML could be accurate tools for MAP classification tasks.