Medicinal and Aromatic Plants Identification Using Machine Learning Methods

Year 2020, , 81 - 87, 31.01.2020

Gökhan Kayhan , Erhan Ergün

https://doi.org/10.17694/bajece.651286

Cited By: 5

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.

Keywords

classification, feature extraction, image processing, , machine learning

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