Automatic identification of ficus deltoidea Jack (Moraceae) varieties based on leaf

Abd. Rasid, Mamat and Nashriyah, Mat and Mohd Nordin, Abdul Rahman (2014) Automatic identification of ficus deltoidea Jack (Moraceae) varieties based on leaf. Modern Applied Science, 8 (5). pp. 121-131. ISSN 19131844 [P]

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Currently, the traditional method used to identify Ficus deltoidea Jack (Moraceae) varieties require the plant taxonomists to observe and examine the leaf morphology of herbarium or live specimens. An automated variety identification system would ease the herbs collector to carry out valuable plant identification work. In this paper, a model for F. deltoidea varieties identification based on their leaf shape, color and texture was developed. Five different varieties of F. deltoidea were used in the proposed work with sixty nine sample data collected for each of varieties. First, the F. deltoidea leaves were plucked and the picture of leaves is then taken by a digital scanner in the format of JPEG. For leaf shape, a total of fourteen shape features were extracted based on basic geometric features. The mean of different color channels was calculated in leaf color feature extraction. Furthermore, four texture features based on gray-level co-occurrence matrix was implemented to extract leaf texture properties. By using the leaf structure, a set of three different leaf properties which are leaf shape, color and texture features was extracted. The features weight is then calculated using eigenvalues coefficient in principal component analysis. The best principal components are retained for identification experiments. Lastly, Nearest Neighbor with Euclidean distance was used in variety identification based on three different leaf properties mentioned above. The effectiveness of different leaf features are demonstrated in the identification experiment.

Item Type: Article
Uncontrolled Keywords: Ficus deltoidea Jack, plant leaf identification, content-based image retrieval, pattern recognition, image processing
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Informatics & Computing
Depositing User: Syahmi Manaf
Date Deposited: 13 Sep 2022 04:33
Last Modified: 13 Sep 2022 04:33

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