Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/2529
Title: Arogya-An Intelligent Ayurvedic Herb Management Platform
Authors: Pathiranage, N
Nilfa, N
Nithmali, M
Kumari, N
Weerasinghe, L
Weerathunga, I
Keywords: Intelligent
Ayurvedic Herb
Management Platform
Arogya
Issue Date: 15-Oct-2020
Publisher: IEEE
Citation: N. Pathiranage, N. Nilfa, M. Nithmali, N. Kumari, L. Weerasinghe and I. Weerathunga, "Arogya -An Intelligent Ayurvedic Herb Management Platform," 2020 61st International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS), 2020, pp. 1-6, doi: 10.1109/ITMS51158.2020.9259290.
Series/Report no.: 2020 61st International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS);Pages 1-6
Abstract: 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.
URI: http://rda.sliit.lk/handle/123456789/2529
ISBN: 978-1-7281-9105-8
Appears in Collections:Department of Computer Science and Software Engineering-Scopes
Research Papers - Dept of Computer Science and Software Engineering
Research Papers - IEEE
Research Papers - IEEE
Research Papers - SLIIT Staff Publications
Research Papers - SLIIT Staff Publications
Research Publications -Dept of Information Technology

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