AI Based Indigenous Medicinal Plant Identification

Anu Paulson, S. Ravishankar
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引用次数: 10

Abstract

In preserving the physical and psychological state of persons, ayurvedic medicines have an important role. The research aims to identify indigenous ayurvedic medicinal plant species using deep learning techniques. The social relevance of the proposal is so high as it would solve the problems of a wide range of stakeholders like physicians, pharmacy, government, and public. The identification of rare plant species may lead to a significant impact on the research associated with medical and other related areas. Another application can be the identification of plant species in forest and remote areas, where access to humans is limited. In such cases, the image of a particular plant species may be captured using drones and further analyzed. Currently, a lot of research work has been going on in the area of plant species identification using machine learning algorithms. The performance of Convolutional Neural Network (CNN), and pretrained models VGG16, and VGG19 has been compared for leaf identification problem. The dataset proposed in this research work contains indigenous medicinal plants of Kerala. The dataset consists of leaf images of 64 medicinal plants. CNN obtained a classification accuracy of 95.79%. VGG16 and VGG19 achieve an accuracy of 97.8% and 97.6% respectively, outperforms basic CNN.
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基于人工智能的本土药用植物鉴定
在保持人的生理和心理状态方面,阿育吠陀药物发挥着重要作用。该研究旨在利用深度学习技术识别本土阿育吠陀药用植物物种。该提案的社会相关性非常高,因为它将解决医生、药房、政府和公众等广泛利益相关者的问题。稀有植物物种的鉴定可能会对医学和其他相关领域的研究产生重大影响。另一个应用是识别森林和偏远地区的植物物种,在那里人类接触有限。在这种情况下,可以使用无人机捕获特定植物物种的图像并进一步分析。目前,在利用机器学习算法进行植物物种识别方面已经进行了大量的研究工作。比较了卷积神经网络(CNN)与预训练模型VGG16和VGG19在叶片识别问题上的性能。本研究工作中提出的数据集包含喀拉拉邦的本土药用植物。该数据集由64种药用植物的叶子图像组成。CNN获得了95.79%的分类准确率。VGG16和VGG19的准确率分别达到97.8%和97.6%,优于基本CNN。
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