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.

中文翻译：

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使用机器学习技术丰富阿育吠陀知识

在印度，每个地区，无论城市还是农村，整个人口都依赖植物来维持生活，包括食物，住所，衣服和药品。由于通货膨胀，合成药物变得负担不起，并且其副作用导致寻求替代药物系统。印度草药及其用途是治疗许多常见疾病的好替代品。使用计算机视觉和机器学习技术，可以根据印度草药的叶子对印度草药进行分类，从而在很大程度上促进印度传统系统-阿育吠陀。在本文中，由尺度不变特征变换（SIFT）组成的系统方法与不同的分类器相结合，该尺度不变特征变换本质上在尺度，照明和旋转方面是统一的。结合支持向量机（SVM），K最近邻（kNN）和朴素贝叶斯分类器，使用SIFT作为共同特征提取器构建了不同的模型。最后，所提出的方法包括具有SIFT特征的特征，该特征具有使用视觉词袋的降维并且通过SVM进行分类。与新建草药数据集和Flavia数据集相比，这项工作得以进行。该模型通过新建的数据集显示了94％的准确性，该数据集由六种印度草药组成。