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Research Article | Open Access |

Identification of Ornamental Plant Functioned as Medicinal Plant Based on Redundant Discrete Wavelet Transformation

Author 1: Kohei Arai Author 2: Indra Nugraha Abdullah Author 3: Hiroshi Okumura
International Journal of Advanced Research in Artificial Intelligence (IJARAI) · Vol. 2, No. 3 · Published 2013 · Cited by 14

DOI: https://doi.org/10.14569/IJARAI.2013.020309

Abstract

Human has a duty to preserve the nature. One of the examples is preserving the ornamental plant. Huge economic value of plant trading, escalating esthetical value of one space and medicine efficacy that contained in a plant are some positive values from this plant. However, only few people know about its medicine efficacy. Considering the easiness to obtain and the medicine efficacy, this plant should be an initial treatment of a simple disease or option towards chemical based medicines. In order to let people get acquaint, we need a system that can proper identify this plant. Therefore, we propose to build a system based on Redundant Discrete Wavelet Transformation (RDWT) through its leaf. Since its character is translation invariant that able to produce some robust features to identify ornamental plant. This system was successfully resulting 95.83% of correct classification rate.

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How to Cite this Article

Arai, K., Abdullah, I. N., & Okumura, H. (2013). Identification of Ornamental Plant Functioned as Medicinal Plant Based on Redundant Discrete Wavelet Transformation. International Journal of Advanced Research in Artificial Intelligence, 2(3). https://doi.org/10.14569/IJARAI.2013.020309

Arai, Kohei, et al.. "Identification of Ornamental Plant Functioned as Medicinal Plant Based on Redundant Discrete Wavelet Transformation." International Journal of Advanced Research in Artificial Intelligence, vol. 2, no. 3, 2013, https://doi.org/10.14569/IJARAI.2013.020309.

@article{Arai2013,
  title     = {Identification of Ornamental Plant Functioned as Medicinal Plant Based on Redundant Discrete Wavelet Transformation},
  journal   = {International Journal of Advanced Research in Artificial Intelligence},
  volume    = {2},
  number    = {3},
  year      = {2013},
  publisher = {The Science and Information Organization},
  author    = {Kohei Arai and Indra Nugraha Abdullah and Hiroshi Okumura},
  doi       = {10.14569/IJARAI.2013.020309},
  url       = {https://doi.org/10.14569/IJARAI.2013.020309}
}

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