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Recognition of ayurvedic medicinal plants from leaves: A computer vision approach

机译:叶子中阿育吠陀药用植物的认识:计算机视觉方法

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Plants are an indispensable part of our ecosystem and India has a long history of using plants as a source of medicines. Since the advent of modern allopathic medicine, the use of traditional medicine declined to a considerable extent. However, in recent years, traditional medicine has made a comeback for a variety of reasons like they are inexpensive, nontoxic and does not impact any side effect. Different kind of medicinal plant species are available on earth but it is very difficult to identify the plant. Considerable knowledge accumulated by the villagers and tribal on medicine from plants remains unknown to the scientists and urban people. This kind of knowledge is usually handed down through generations. Our immediate concern is to preserve this knowledge in digital form through the concepts of machine learning, pattern recognition and computer vision. A machine can identify a medicinal plant through the features extracted from the leaf images, together with a classification algorithm. This paper proposes a computer vision approach for the recognition of ayurvedic medicinal plant species found in Western Ghats of India. The proposed system uses a combination of SURF and HOG features extracted from leaf images and a classification using k-NN classifier. Our experiments show results which seem to be sufficient for building apps for real life use.
机译:植物是我们生态系统的不可或缺的一部分,印度具有悠久的历史,使用植物作为药物来源。自现代疗法的出现以来,传统医学的使用下降到相当程度。然而,近年来,传统医学已经出于各种原因卷土重来,它们是廉价的,无毒的并且不会影响任何副作用。地球上有不同种类的药用植物物种,但识别植物很难。村民和部落对来自植物的医学累积的相当大的知识仍然是科学家和城市人民的未知。这种知识通常经过几代人。我们的直接关注是通过机器学习,模式识别和计算机愿景的概念来保​​护数字形式的这种知识。一种机器可以通过从叶片图像提取的特征与分类算法一起识别药用植物。本文提出了一种识别印度西戈阿尔吠陀药用植物物种的计算机视觉方法。所提出的系统使用从叶片图像中提取的冲浪和猪的组合和使用K-NN分类器的分类。我们的实验表明了似乎足以建立真实生活的应用程序。

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