Herbal Plant Analysis Based on Leaf Features using K-Means Clustering

Abstract

Abstract Plants are essential in the Earth, as it supplies the oxygen needed by human beings and animals, and becomes the source of foods and medical treatments. Many medicinal plants can treat diseases and it is also called herbal plants. Traditionally, these plants are processed and transformed as traditional medicines to cure any diseases. Nowadays, there are still practices that use medicinal plants. However, it is quite challenging to find herbal plants and these herbal plants come with different features such as size, shape, and colour. Therefore, this paper presents a machine learning approach, namely clustering, to classify the herbal plant species through images. We focused on six herbal plants in Malaysia which are Peacock Fern, Misai Adam, Mempisang, Tapak Sulaiman, Pandan Serapat and Kacip Fatimah. These species were collected from Taman Negara Pahang, Kuala Keniam, Malaysia. The k-means algorithm was employed by experimenting with several numbers of clusters in the range of two, three, four and five. The features extracted were colour and shape and this was performed using Python libraries. This study would benefit the researchers or botanists to identify the plant’s name based on the features. In order to give more interactive elements, a dashboard was developed in which the herbal plants are categorized in the group that has similar characteristics.