Automatic identification for field butterflies by convolutional neural networks
In today’s competitive conditions, producing fast, inexpensive and reliable solutions are an objective for engineers. Development of artificial intelligence and the introduction of this technology to almost all areas have created a need to minimize the human factor by using artificial intelligence i...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2020-02-01
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| Series: | Engineering Science and Technology, an International Journal |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215098619326011 |
| _version_ | 1818879768678891520 |
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| author | Ayad Saad Almryad Hakan Kutucu |
| author_facet | Ayad Saad Almryad Hakan Kutucu |
| author_sort | Ayad Saad Almryad |
| collection | DOAJ |
| description | In today’s competitive conditions, producing fast, inexpensive and reliable solutions are an objective for engineers. Development of artificial intelligence and the introduction of this technology to almost all areas have created a need to minimize the human factor by using artificial intelligence in the field of image processing, as well as to make a profit in terms of time and labor. In this paper, we propose an automated butterfly species identification model using deep neural networks. We collected 44,659 images of 104 different butterfly species taken with different positions of butterflies, the shooting angle, butterfly distance, occlusion and background complexity in the field in Turkey. Since many species have a few image samples we constructed a field-based dataset of 17,769 butterflies with 10 species. Convolutional Neural Networks (CNNs) were used for the identification of butterfly species. Comparison and evaluation of the experimental results obtained using three different network structures are conducted. Experimental results on 10 common butterfly species showed that our method successfully identified various butterfly species. Keywords: Butterfly, Deep learning, Classification, ResNet, Transfer learning |
| first_indexed | 2024-12-19T14:35:20Z |
| format | Article |
| id | doaj.art-9ba1a8b7ad004434ba995de87acee191 |
| institution | Directory Open Access Journal |
| issn | 2215-0986 |
| language | English |
| last_indexed | 2024-12-19T14:35:20Z |
| publishDate | 2020-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Engineering Science and Technology, an International Journal |
| spelling | doaj.art-9ba1a8b7ad004434ba995de87acee1912022-12-21T20:17:18ZengElsevierEngineering Science and Technology, an International Journal2215-09862020-02-01231189195Automatic identification for field butterflies by convolutional neural networksAyad Saad Almryad0Hakan Kutucu1Corresponding author.; Department of Computer Engineering, Karabuk University, Karabuk, TurkeyDepartment of Computer Engineering, Karabuk University, Karabuk, TurkeyIn today’s competitive conditions, producing fast, inexpensive and reliable solutions are an objective for engineers. Development of artificial intelligence and the introduction of this technology to almost all areas have created a need to minimize the human factor by using artificial intelligence in the field of image processing, as well as to make a profit in terms of time and labor. In this paper, we propose an automated butterfly species identification model using deep neural networks. We collected 44,659 images of 104 different butterfly species taken with different positions of butterflies, the shooting angle, butterfly distance, occlusion and background complexity in the field in Turkey. Since many species have a few image samples we constructed a field-based dataset of 17,769 butterflies with 10 species. Convolutional Neural Networks (CNNs) were used for the identification of butterfly species. Comparison and evaluation of the experimental results obtained using three different network structures are conducted. Experimental results on 10 common butterfly species showed that our method successfully identified various butterfly species. Keywords: Butterfly, Deep learning, Classification, ResNet, Transfer learninghttp://www.sciencedirect.com/science/article/pii/S2215098619326011 |
| spellingShingle | Ayad Saad Almryad Hakan Kutucu Automatic identification for field butterflies by convolutional neural networks Engineering Science and Technology, an International Journal |
| title | Automatic identification for field butterflies by convolutional neural networks |
| title_full | Automatic identification for field butterflies by convolutional neural networks |
| title_fullStr | Automatic identification for field butterflies by convolutional neural networks |
| title_full_unstemmed | Automatic identification for field butterflies by convolutional neural networks |
| title_short | Automatic identification for field butterflies by convolutional neural networks |
| title_sort | automatic identification for field butterflies by convolutional neural networks |
| url | http://www.sciencedirect.com/science/article/pii/S2215098619326011 |
| work_keys_str_mv | AT ayadsaadalmryad automaticidentificationforfieldbutterfliesbyconvolutionalneuralnetworks AT hakankutucu automaticidentificationforfieldbutterfliesbyconvolutionalneuralnetworks |