Development of Feature Extraction on Leaf Image for Medicinal Plants Identification
Trinugi Wira Harjanti1, Sarifuddin Madenda2
1Trinugi Wira Harjanti, Department of Information System, STTI NIIT, Indonesia.
2Sarifuddin Madenda, Department of Computer Science & Information Technology, Gunadarma University, Indonesia.
Manuscript received on 03 August 2019 | Revised Manuscript received on 26 August 2019 | Manuscript Published on 05 September 2019 | PP: 163-168 | Volume-8 Issue-2S7 July 2019 | Retrieval Number: B10400782S719/2019©BEIESP | DOI: 10.35940/ijrte.B1040.0782S719
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The leaf image identification process depends on the feature extraction results. Each medicinal plant has different shapes and patterns of leaf venation. But for one type of medicinal plants have the same pattern of venation shape and pattern even though the size is different. One of the methods for extraction of leaf image form characteristics is by fractal-based feature extraction. Through fractal can be calculated the value of leaf dimensions and searched parts of leaves that have similarities between one part with other parts. As for the method of extracting the characteristics of venation pattern using B-Spline method.Benefits of research conducted is to help people identifying the types of medicinal plants found, knowing the benefits and ways of brewing. While the research contribution is prototype software application based on information technology that can be used by the people through mobile phones for the identification of medicinal plants. To identify or match the results of feature extraction on the leaf found whether included in the medicinal plant, conducted by Euclidean Distance method. In the experiments we used 1100 data consist of 55 variety of medicinal plants for each 20 samples.The experimental result show that the accuracy of identification using of fractal and b-spline is 85.30%.
Keywords: Feature Extraction, Medicinal Plant Identification, Fractal, B-spline, Euclidean Distance.
Scope of the Article: Image Security