Enrich Ayurveda knowledge using machine learning techniques
In India, every region, urban or rural the whole population is dependent on plants for life sustenance in the form of food, shelter, clothes and medicines. Due to inflation, synthetic medicines have become less affordable and their side effect has led in seeking alternative medication system. Indian medicinal herbs and its uses are good alternates for curing many common ailments and diseases. Using computer vision and machine learning techniques, the Indian medicinal herbs can be classified based on their leaves and thus promote the Indian traditional system – Ayurveda to a great extent. In this paper, a systematic approach consisting of Scale Invariant Feature Transform (SIFT) which is uniform in nature to scale, illumination and rotation is combined with different classifiers. Different models are built using SIFT as the common feature extractor in combination with Support Vector Machine (SVM), K-Nearest Neighbor (kNN) and Naive Bayes Classifier. Finally, the proposed method consists of SIFT features with dimension reduction using Bag of Visual Words and classified by SVM. The work is carried over in comparison with newly built herb dataset and Flavia dataset. The model shows an accuracy of 94% with newly built dataset which consists of six Indian medicinal herbs.
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