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Butterfly species recognition using Artificial Neural Network (ANN)

Butterfly species recognition using Artificial Neural Network (ANN) / Yong Kai Xin
Dalam tahun 2017, terdapat lebih kurang 20,000 spesies rama-rama telah ditemui dalam seluruh dunia. Rama-rama terkenal dengan corak sayapnya yang cantik dan kebaikannya kepada alam sekitar. Dalam pengajian ini, pengecaman spesies rama-rama diautomatikkan dengan menggunakan kecerdasan buatan. Corak yang terdapat di atas sayap rama-rama digunakan sebagai parameter untuk menentukan spesies rama-rama tersebut. Gambar rama-rama ditangkap dan latar belakang gambar tersebut dikeluarkan untuk menyenangkan proses pengecaman. Seterusnya, penghurai corak binari buatan (LBP) digunakan ke atas gambar yang telah diproses. Sebuah histogram yang mengandungi informasi gambar akan terhasil. Rangkaian neural buatan (ANN) pula digunakan untuk mengklasifikasikan gambar tersebut. _______________________________________________________________________________________________________ In 2017, there are about 20,000 species of butterfly has been discovered all over the world. Butterfly is well known because of its beautiful wings pattern and its benefits to the environment. In this research, butterfly species recognition is automated using artificial intelligence. Pattern on the butterfly wings is used as a parameter to determine the species of the butterfly. The butterfly image is captured and the background of the image is removed to make the recognition process easier. Local binary pattern (LBP) descriptor is then applied to the processed image and a histogram consist of image information is computed. Artificial neural network (ANN) is used to classify the image.
Contributor(s):
Yong Kai Xin - Author
Primary Item Type:
Final Year Project
Identifiers:
Accession Number : 875007148
Barcode : 00003107026
Language:
English
Subject Keywords:
butterfly species recognition; Local binary pattern; Artificial neural network
First presented to the public:
6/1/2017
Original Publication Date:
4/18/2018
Previously Published By:
Universiti Sains Malaysia
Place Of Publication:
School of Electrical & Electronic Engineering
Citation:
Extents:
Number of Pages - 82
License Grantor / Date Granted:
  / ( View License )
Date Deposited
2018-04-18 12:40:03.662
Date Last Updated
2019-01-07 11:24:32.9118
Submitter:
Mohd Jasnizam Mohd Salleh

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