Publication Type : Journal Article
Publisher : Advances in Intelligent Systems and Computing.
Source : Advances in Intelligent Systems and Computing, Volume 530, p.177-191 (2016).
Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-84989964199&partnerID=40&md5=1351a98e1101f9196854b7144cc87a38
Campus : Coimbatore
School : School of Engineering
Department : Computer Science
Year : 2016
Abstract : India has a vast history of using plants as a source of medicines. This science is termed as Ayurveda. But, sadly somewhere in the race of keeping up with medicinal science and technology, India as a country has lost its track in the field of Ayurveda. Researchers and medicinal practitioners today, in spite of knowing that allopathic medicines are made using certain plant extracts, are oblivious about the medicinal the properties of plants. This paper aims at eradicating this problem, and hence strives to help potential users make better use of plants with medicinal properties. The dataset consists of 300 images of different types of leaves. The classification of the leaves is done with the help of a decision tree. Our system is an easy to use application which is fast in execution too. The objective of doing this paper is to develop an application for leaf recognition for retrieving the medicinal properties of plants. The recognition of leaves is done by extracting the features of the leaves from the images. The primary stakeholders involved with this project are researchers, medical practitioners and people with a keen interest in botany. We believe that this application will be an important part of the mentioned stakeholders’ daily lives. The primary purpose that this paper serves is to solve the problem of not knowing the useful properties of many plants. © Springer International Publishing AG 2016.
Cite this Research Publication : D. Venkataraman, Narasimhan, S., Shankar, N., S. Sidharth, V., and D. Prasath, H., “Leaf recognition algorithm for retrieving medicinal information”, Advances in Intelligent Systems and Computing, vol. 530, pp. 177-191, 2016.