Recognition of Medicinal Leaves using PCA & SVM
Sampath Kumar B1, Varsha N2, Zaara FK3
1Sampath Kumar B, Assistant Professor, Department of Electronics and Communication Engineering, Gowdara Mallikarjunappa Institute of Technology, Karur Industrial Area, PB Road, Davanagere, Karnataka, India.
2Varsha N, Student, Department of Electronics and Communication Engineering, Proudha Deveraya Institute of Technology, Sha Bhavarlal Babulal Nahar Campus, Hosapete, Karnataka, India.
3Zaara FK, Student, Department of Electronics and Communication Engineering, Proudha Deveraya Institute of Technology, Sha Bhavarlal Babulal Nahar Campus, Hosapete, Karnataka, India.
Manuscript received on 20 September 2015 | Revised Manuscript received on 30 September 2015 | Manuscript published on 30 September 2015 | PP: 45-48 | Volume-4 Issue-4, September 2015 | Retrieval Number: D1479094415©BEIESP
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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: In pharmacological science and bhaishaja kalpana, the recognition of plant leaves(leaf) is very important to make medicine. A Recognizing plant leaf has so far been an important and difficult task. In this paper leaf recognition system using the support vector machine as a classifier is proposed. The leaf recognition system consists of image acquisition, preprocessing, feature extraction and classification. The preprocessing involves a typical image processing conversion such as transforming color images to gray scale image then binary , smoothing and to contour image etc. In the feature extraction involves geometrical features are extracted from leaf images such as diameter, length, area, and perimeter etc. Total 10 digital morphological features (DMF) including geometrical features. These 10 features are orthogonalized into five principal variables using principal component analysis (PCA). These features are given as input vector to the support vector machine (SVM) for classifying leaves.
Keywords: Digital Morphological Feature(DMF); leaves(Leeaf) Recognition; Principle component analysis (PCA); Support Vector Machine (SVM)
Scope of the Article: Artificial Intelligence and Machine Learning