Publication Type : Journal Article
Publisher : International Journal of Applied Engineering Research
Source : International Journal of Applied Engineering Research, Volume 11, Issue 7, Pages 5142-5147, 1 May 2016.
Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-84966290822&partnerID=40&md5=965d28e21463e82763e217c624cd3841
Keywords : Ayurvedic leaf classification, Feature extraction, Leaf factor
Campus : Mysuru
School : School of Arts and Sciences
Department : Computer Science
Year : 2016
Abstract : Automatic recognition of plant species recognition is a challenging problem in the area of pattern recognition and computer vision. An efficient plant recognition system will be beneficial to many sectors of society which includes medical field, botanic researches and plant taxonomy study. Manual identification process requires prior knowledge and also it is a lengthy process. This paper proposes a simple and efficient methodology for Ayurvedic plant classification using digital image processing and machine vision technology. The three major phases in proposed methodology are pre-processing, feature extraction and classification. Pre-processing is done in order to highlight the relevant features to be used in the proposed methodology as well as to reduce unwanted noise from the input image, which reduces the chance of getting optimal feature values. In feature extraction phase, different morphologic features such as mean, standard deviation, convex hull ratio, isoperimetric quotient, eccentricity and entropy are extracted from the pre-processed leaf image. In the third phase, a new approach to classify ayurvedic plant species is adopted to recognize plant species by calculating the leaf factor of the input leaf using the extracted feature values and it is compared with the trained values that are stored in the database. An accuracy of 93.75% is obtained for the proposed methodology. © Research India Publications.
Cite this Research Publication : Pushpa, B.R., Anand, C., Mithun Nambiar, P., "169Ayurvedic plant species recognition using statistical parameters on leaf images," International Journal of Applied Engineering Research, 11 (7), pp. 5142-5147, 1 May 2016.