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AI Based Indigenous Medicinal Plant Identification

机译:基于AI的本土药用植物鉴定

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摘要

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.
机译:在维持人的身心状态中,阿育吠陀药具有重要作用。该研究旨在使用深度学习技术来识别本土的阿育吠陀药用植物物种。该提案的社会相关性很高,可以解决众多利益相关者(例如医生,药房,政府和公众)的问题。稀有植物物种的鉴定可能对医学和其他相关领域的研究产生重大影响。另一个应用可以是在人类接触受限的森林和偏远地区识别植物物种。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行进一步分析。当前,在使用机器学习算法的植物物种识别领域中,已经进行了许多研究工作。卷积神经网络(CNN)以及预训练模型VGG16和VGG19的性能已针对叶子识别问题进行了比较。这项研究工作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种药用植物的叶片图像组成。 CNN的分类准确率为95.79%。 VGG16和VGG19的准确度分别达到97.8%和97.6%,优于基本的CNN。

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