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Arogya -An Intelligent Ayurvedic Herb Management Platform

机译:Arogya-阿育吠陀智能草药管理平台

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Ayurvedic means a science of life and well-being with its unique approaches to social and spiritual life. Especially in Sri Lanka we have our own set of rare Ayurvedic herbs which have been utilized by generations as medicinal treatments for a variety of diseases. Absence of specialists in this area makes proper identification as well as classification of valuable herbal plants a tedious task, which is essential for better treatment. Hence, a fully automated system for herb detection and classification, information visualization regarding them is highly desirable. There are existing applications which can identify plants with low prediction accuracies, as well as to give information regarding them. However, these applications are based on foreign plant data sets that do not include valuable herbs and shrubs with medicinal qualities. Hence this research proposes an application unique to medicinal plants, which can perform all these functionalities in both online and offline approach. Here, a new Ayurvedic plant dataset prepared from scratch, and preliminary results for classification of 5 types of herbs, compared with several deep Convolutional Neural Network (CNN) models based on transfer learning are presented. Experimental results indicate Marker-based Watershed algorithm as the best object detection algorithm in a complex background, VGG-16 as the best deep CNN classification model which reached a promising testing accuracy of 99.53%, and Seq2Seq LSTM model as the best deep learning model with optimum accuracy in abstractive information summarization.
机译:阿育吠陀疗法是一门关于生命和幸福的科学,它以独特的方式对待社会和精神生活。特别是在斯里兰卡,我们拥有自己的一套稀有的印度草药,已被世世代代用于多种疾病的药物治疗。由于缺少该领域的专家,因此正确鉴定以及对珍贵的草药植物进行分类是一项繁琐的任务,这对于更好的治疗至关重要。因此,非常需要用于草药检测和分类,关于它们的信息可视化的全自动系统。现有的应用程序可以识别具有较低预测精度的植物,并提供有关它们的信息。但是,这些应用程序基于外国植物数据集,其中不包括有价值的具有药用特性的草药和灌木。因此,本研究提出了一种药用植物独有的应用程序,它可以在线和离线方式执行所有这些功能。在这里,与几种基于转移学习的深层卷积神经网络(CNN)模型相比,本文提供了从头开始准备的新印度草药植物数据集以及5种草药分类的初步结果。实验结果表明,基于Marker的分水岭算法是复杂背景下的最佳对象检测算法,VGG-16是最佳的深度CNN分类模型,可达到99.53%的良好测试精度,而Seq2Seq LSTM模型则是最佳的深度学习模型,具有以下优点:抽象信息摘要中的最佳准确性。

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