A Revolutionary Machine-Learning based approach for identifying Ayurvedic Medicinal Plants

Subhashree Darshana, Kasturi Soumyakanta
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Abstract

Developing an automated classification system for medicinal herbs is indeed a time-consuming and complicated task. Plants have been used for medicinal purposes for millennia. Ayurvedic herbs are gaining popularity in the medical industry due to fewer dangerous side effects and lower costs compared to modern pharmaceuticals. According to these facts, we have expressed a strong interest in the discovery research of Ayurvedic herbal medicines. This study examines theefficiency and reliability of several algorithms of machine learning for plant classification based on photos of leaves used in current history. Assessments of their benefits and drawbacks are also presented. The paper includes image processing algorithms that are used to recognize leaf and obtain significant leaf properties for particular machine learning approaches.
一种革命性的基于机器学习的方法来识别阿育吠陀药用植物
开发中药自动分类系统确实是一项耗时且复杂的任务。几千年来,植物一直被用作药用。阿育吠陀草药在医疗行业越来越受欢迎,因为与现代药物相比,它的危险副作用更少,成本更低。根据这些事实,我们对阿育吠陀草药的发现研究表达了浓厚的兴趣。本研究考察了几种基于当前历史上使用的叶子照片的机器学习植物分类算法的效率和可靠性。并对其优缺点进行了评价。本文包括用于识别叶子的图像处理算法,并为特定的机器学习方法获得重要的叶子属性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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