Automatic identification of ficus deltoidea Jack (Moraceae) varieties based on leaf
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| collectionurl | https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 |
| date | 2014-10-28 16:15:06 |
| format | Restricted Document |
| id | 11325 |
| institution | UniSZA |
| internalnotes | Ab Jabal, M.F., Hamid, S., Shuib, S., & Ahmad, I. (2013). Leaf features extraction and recognition approaches to classify plant. Journal of Computer Science, 9(10), 1295-1304. http://dx.doi.org/10.3844/jcssp.2013.1295.1304 Abdulla, M. A., Ahmed, K. A. A., Abu-Luhoom, F. M., & Muhanid, M. (2010). Role of Ficus deltoidea extract in the enhancement of wound healing in experimental rats. Biomedical Research, 21(3), 241-245. Adam, Z., Ismail, A., Khamis, S., Mokhtar, M. H. M., & Hamid, M. (2011). Antihyperglycemic activity of F. deltoidea ethanolic extract in normal rats. Sains Malaysiana, 40(5), 489-495. Cope, J. S., Corney, D., Clark, J. Y., Remagnino, P., & Wilkin, P. (2012). Plant species identification using digital morphometrics: A review. Experts Systems with Applications, 39(8), 7562-7573. http://dx.doi.org/10.1016/j.eswa.2012.01.073 Du, J. X., Wang, X. F., & Zhang, G. J. (2007). Leaf shape based species recognition. Applied Mathematics and Computation, 185(2), 883-893. http://dx.doi.org/10.1016/j.amc.2006.07.072 Fotopoulou, F., Laskaris, N., Economou, G., & Fotopoulos, S. (2013). Advanced leaf image retrieval via Multidimensional Embedding Sequence Similarity (MESS) method. Pattern Anal. Applic, 16(3), 381-392. http://dx.doi.org/10.1007/s10044-011-0254-6 Hean, C. O., Rosnaini, M. Z., & Pozi, M. (2011). Traditional knowledge of medicinal plants among the Malay villagers in kampung Mak Kemas, Terengganu, Malaysia. Studies on Ethno-Medicine, 5(3), 175-185. Hossain, J., & Amin, M. A. (2010). Leaf shape identification based plant biometrics. Proceedings of the Computer and Information Technology, 458-463. http://dx.doi.org/10.1109/ICCITECHN.2010.5723901 Kadir, A., Nugroho, L.E., Susanto, A., & Santosa, P. I. (2011). A comparative experiment of several shape methods in recognizing plants. Int. J. Comput. Sci. Inform. Technol., 3(3), 256-263. http://dx.doi.org/10.5121/ijcsit.2011.3318 Kadir, A., Nugroho, L. E., Susanto, A., & Santosa, P. I. (2012). Performance improvement of leaf identification system using principal component analysis. International Journal of Advanced Science and Technology, 44, 113-124. Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20(1), 141-151. http://dx.doi.org/10.1177/001316446002000116 Kochummen, K. M., & Rusea, G. (2000). Tree Flora of Sabah and Sarawak (Vol. 3, pp. 181-334). Lansky, E. P., & Paavilainen, H. M. (2011). Figs: The Genus Ficus. London New York: CRC Press Boca Raton. Lee, C. L., & Chen, S. Y. (2006). Classification of leaf images. International Journal of Imaging Systems and Technology, 16(1), 15-23. http://dx.doi.org/10.1002/ima.20063 Najjar, A., & Zagrouba, E. (2012). Flower image segmentation based on color analysis and a supervised evaluation. Proceedings of the Communications and Information Technology, 397-401. http://dx.doi.org/10.1109/ICCITechnol.2012.6285834 Nihayah, M., Yong, K. W., & Nur Faizah, A. B. (2012). Determination of mineral content in the Ficus deltoidea leaves. Jurnal Sains Kesihatan Malaysia, 10(2), 25-29. Norhaniza, A., Sin, C. Y., Chee, E. S., Nee, K. I., & Renxin, L. (2007). Blood glucose lowering effect of Ficus deltoidea aqueous extract. Malaysian Journal of Science, 26(1), 73-78. O’Rourke, N., Hatcher, L., & Stepanski, E. J. (2005). A step-by-step approach to using SAS for univariate & multivariate statistics. SAS Publishing. Ramlan, A. A. (2003). Turning Malaysia into a global herbal producer: A personal perspective. Retrieved from http://www.penerbit.utm.my/syarahan/pdf/09/siri9_teks.pdf Rencher, A. C. (2002). Methods of Multivariate Analysis. Wiley-Interscience. Sulaiman, M. R., Hussain, M. K., Zakaria, Z. A., Somchit, M. N., Moin, S., Mohamad, A. S., & Israf, D. A. (2008). Evaluation of the antinociceptive activity of Ficus deltoidea aqueous extract. Fitoterapia, 79(7-8), 557-561. http://dx.doi.org/10. 1016/j.fitote.2008.06.005 Turner, I. M. (1995). Catalogue of the vascular plants in Malaya. Garden’s Bulletin Singapore (Vol. 47, pp. 347-757). Singapore Botanic Gardens. USDA (2007). ARS, National Genetic Resources Program, Germplasm Resources Information Network – (GRIN) Database. Retrieved from http://ars-grin.gov/cgi-bin/npgs/html/taxon.pl?16826 Wu, S. G., Bao, F. S., Xu, E. U., Wang, Y. X., Chang, Y. F., & Xiang, Q. L. (2007). A leaf recognition algorithm for plant classification using probabilistic neural network. Proceedings of the Signal Processing and Information Technology, 11-16. http://dx.doi.org/10.1109/ISSPIT.2007.4458016 Zulkifli, Z., Puteh, S., & Mohtar, I. A. (2011). Plant leaf identification using moment invariants and general regression neural network. Proceedings of the Hybrid Intelligent Systems, 430-435. http://dx.doi.org/10.1109/HIS.2011.6122144 |
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| resourceurl | https://intelek.unisza.edu.my/intelek/pages/view.php?ref=11325 |
| spelling | 11325 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=11325 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal UniSZA Unisza unisza image/jpeg inches 96 96 1413 748 65 65 2014-10-28 16:15:06 1413x748 5542-01-FH02-FIK-14-01593.jpg UniSZA Private Access Automatic identification of ficus deltoidea Jack (Moraceae) varieties based on leaf Modern Applied Science 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. 8 5 Canadian Center of Science and Education Canadian Center of Science and Education 121-131 Ab Jabal, M.F., Hamid, S., Shuib, S., & Ahmad, I. (2013). Leaf features extraction and recognition approaches to classify plant. Journal of Computer Science, 9(10), 1295-1304. http://dx.doi.org/10.3844/jcssp.2013.1295.1304 Abdulla, M. A., Ahmed, K. A. A., Abu-Luhoom, F. M., & Muhanid, M. (2010). Role of Ficus deltoidea extract in the enhancement of wound healing in experimental rats. Biomedical Research, 21(3), 241-245. Adam, Z., Ismail, A., Khamis, S., Mokhtar, M. H. M., & Hamid, M. (2011). Antihyperglycemic activity of F. deltoidea ethanolic extract in normal rats. Sains Malaysiana, 40(5), 489-495. Cope, J. S., Corney, D., Clark, J. Y., Remagnino, P., & Wilkin, P. (2012). Plant species identification using digital morphometrics: A review. Experts Systems with Applications, 39(8), 7562-7573. http://dx.doi.org/10.1016/j.eswa.2012.01.073 Du, J. X., Wang, X. F., & Zhang, G. J. (2007). Leaf shape based species recognition. Applied Mathematics and Computation, 185(2), 883-893. http://dx.doi.org/10.1016/j.amc.2006.07.072 Fotopoulou, F., Laskaris, N., Economou, G., & Fotopoulos, S. (2013). Advanced leaf image retrieval via Multidimensional Embedding Sequence Similarity (MESS) method. Pattern Anal. Applic, 16(3), 381-392. http://dx.doi.org/10.1007/s10044-011-0254-6 Hean, C. O., Rosnaini, M. Z., & Pozi, M. (2011). Traditional knowledge of medicinal plants among the Malay villagers in kampung Mak Kemas, Terengganu, Malaysia. Studies on Ethno-Medicine, 5(3), 175-185. Hossain, J., & Amin, M. A. (2010). Leaf shape identification based plant biometrics. Proceedings of the Computer and Information Technology, 458-463. http://dx.doi.org/10.1109/ICCITECHN.2010.5723901 Kadir, A., Nugroho, L.E., Susanto, A., & Santosa, P. I. (2011). A comparative experiment of several shape methods in recognizing plants. Int. J. Comput. Sci. Inform. Technol., 3(3), 256-263. http://dx.doi.org/10.5121/ijcsit.2011.3318 Kadir, A., Nugroho, L. E., Susanto, A., & Santosa, P. I. (2012). Performance improvement of leaf identification system using principal component analysis. International Journal of Advanced Science and Technology, 44, 113-124. Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20(1), 141-151. http://dx.doi.org/10.1177/001316446002000116 Kochummen, K. M., & Rusea, G. (2000). Tree Flora of Sabah and Sarawak (Vol. 3, pp. 181-334). Lansky, E. P., & Paavilainen, H. M. (2011). Figs: The Genus Ficus. London New York: CRC Press Boca Raton. Lee, C. L., & Chen, S. Y. (2006). Classification of leaf images. International Journal of Imaging Systems and Technology, 16(1), 15-23. http://dx.doi.org/10.1002/ima.20063 Najjar, A., & Zagrouba, E. (2012). Flower image segmentation based on color analysis and a supervised evaluation. Proceedings of the Communications and Information Technology, 397-401. http://dx.doi.org/10.1109/ICCITechnol.2012.6285834 Nihayah, M., Yong, K. W., & Nur Faizah, A. B. (2012). Determination of mineral content in the Ficus deltoidea leaves. Jurnal Sains Kesihatan Malaysia, 10(2), 25-29. Norhaniza, A., Sin, C. Y., Chee, E. S., Nee, K. I., & Renxin, L. (2007). Blood glucose lowering effect of Ficus deltoidea aqueous extract. Malaysian Journal of Science, 26(1), 73-78. O’Rourke, N., Hatcher, L., & Stepanski, E. J. (2005). A step-by-step approach to using SAS for univariate & multivariate statistics. SAS Publishing. Ramlan, A. A. (2003). Turning Malaysia into a global herbal producer: A personal perspective. Retrieved from http://www.penerbit.utm.my/syarahan/pdf/09/siri9_teks.pdf Rencher, A. C. (2002). Methods of Multivariate Analysis. Wiley-Interscience. Sulaiman, M. R., Hussain, M. K., Zakaria, Z. A., Somchit, M. N., Moin, S., Mohamad, A. S., & Israf, D. A. (2008). Evaluation of the antinociceptive activity of Ficus deltoidea aqueous extract. Fitoterapia, 79(7-8), 557-561. http://dx.doi.org/10. 1016/j.fitote.2008.06.005 Turner, I. M. (1995). Catalogue of the vascular plants in Malaya. Garden’s Bulletin Singapore (Vol. 47, pp. 347-757). Singapore Botanic Gardens. USDA (2007). ARS, National Genetic Resources Program, Germplasm Resources Information Network – (GRIN) Database. Retrieved from http://ars-grin.gov/cgi-bin/npgs/html/taxon.pl?16826 Wu, S. G., Bao, F. S., Xu, E. U., Wang, Y. X., Chang, Y. F., & Xiang, Q. L. (2007). A leaf recognition algorithm for plant classification using probabilistic neural network. Proceedings of the Signal Processing and Information Technology, 11-16. http://dx.doi.org/10.1109/ISSPIT.2007.4458016 Zulkifli, Z., Puteh, S., & Mohtar, I. A. (2011). Plant leaf identification using moment invariants and general regression neural network. Proceedings of the Hybrid Intelligent Systems, 430-435. http://dx.doi.org/10.1109/HIS.2011.6122144 |
| spellingShingle | Automatic identification of ficus deltoidea Jack (Moraceae) varieties based on leaf |
| summary | 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. |
| title | Automatic identification of ficus deltoidea Jack (Moraceae) varieties based on leaf |
| title_full | Automatic identification of ficus deltoidea Jack (Moraceae) varieties based on leaf |
| title_fullStr | Automatic identification of ficus deltoidea Jack (Moraceae) varieties based on leaf |
| title_full_unstemmed | Automatic identification of ficus deltoidea Jack (Moraceae) varieties based on leaf |
| title_short | Automatic identification of ficus deltoidea Jack (Moraceae) varieties based on leaf |
| title_sort | automatic identification of ficus deltoidea jack (moraceae) varieties based on leaf |