Öz
Kaynakça
- Bilecenoglu M, Taskavak E, Mater S & Kaya M (2002). Checklist of the marine fishes of Turkey. Zootaxa 113(1): 1-194
- Colab (2021). Google Colaboratory. Retrieved in November, 24, 2021 from https://colab.research.google.com
- Hridayami P, Putra I K G D & Wibawa K S (2019). Fish species recognition using VGG16 deep convolutional neural network. Journal of Computing Science and Engineering 13(3): 124-130
- Khalifa N E M, Taha M H N & Hassanien A E (2018). Aquarium Family Fish Species Identification System Using Deep Neural Networks, Advances in Intelligent Systems and Computing Proceedings of the International Conference on Advanced Intelligent Systems and Informatics, 1-3 Sep, Cham, pp. 347-356
- Kratzert F & Mader H (2018). Fish species classification in underwater video monitoring using Convolutional Neural Networks, EarthArXiv 15 (5) : 1-8
- Lecun Y, Bengio Y & Hinton G (2015). Deep learning. Nature 521: 436-444
- Majumder A, Rajbongshi A, Rahman M M & Biswas A A (2021). Local freshwater fish recognition using different cnn architectures with transfer learning. International Journal on Advanced Science, Engineering and Information Technology 11(3): 1078-1083
- Mingwang L (2017). Fish Image Recognition and Separation Based on Convolutional Neural Network[J]. Image and Multimedia Technology 3: 82-83
- Montalbo F J P & Hernandez A A (2019). Classification of Fish Species with Augmented Data using Deep Convolutional Neural Network. 2019 IEEE 9th International Conference on System Engineering and Technology (ICSET), 7-7 Oct, Shah Alam, pp. 396-401
- Rathi D, Jain S & Indu S (2017). Underwater fish species classification using convolutional neural network and deep learning. 2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR), 27-30 Dec, Bangalore, pp. 1-6
- Rauf H T, Lali M I U, Zahoor S, Shah S Z H, Rehman A U & Bukhari S A C (2019). Visual features based automated identification of fish species using deep convolutional neural networks. Computers and electronics in agriculture 167: 105075
- Santos A A D & Gonçalves W N (2019). Improving Pantanal fish species recognition through taxonomic ranks in convolutional neural networks. Ecological Informatics 53: 100977
- Sarigül M, & Avci M (2017). Comparison of different deep structures for fish classification. International Journal of Computer Theory and Engineering 9(5): 362-366
- Shah S Z H, Rauf H T, IkramUllah M, Khalid M S, Farooq M, Fatima M & Bukhari S A C (2019). Fish-Pak: Fish species dataset from Pakistan for visual features based classification. Data in brief 27: 104565
- Simonyan K & Zisserman A (2014). Very deep convolutional networks for large-scale image recognition. arXiv: 1409.1556
- Song H A & Lee S Y (2013). Hierarchical representation using NMF. In: International conference on neural information processing, Berlin, Heidelberg, pp. 466–473
- Ulucan O, Karakaya D & Turkan M (2020). A large-scale dataset for fish segmentation and classification. 2020 Innovations in Intelligent Systems and Applications Conference (ASYU), 15-17 Oct, Istanbul, pp. 1-5
- Zhang B, Xie F & Han F (2019). Fish Population Status Detection Based on Deep Learning System. In 2019 IEEE International Conference on Mechatronics and Automation (ICMA), 4-7 Aug, Tianjin, pp. 81-85
Öz
In the classification of fish, both knowledge and great effort are required to determine the characteristics of fish. Traditionally, however, manual classification of extrinsic characteristics of different fish species has been a difficult and time-consuming process due to their close resemblance to each other. Recently, deep learning methods used in the light of developments in the field of
computer vision have facilitated the training of fish image classification models and the recognition of various fish species. In this study, a new convolutional neural network model classifying 8 different belonging to 6 families (Mullidae, Sparidae, Carangidae, Serranidae, Clupeidae, Salmonidae) fish species using deep learning methods was proposed. The species include Clupeonella
cultriventris N., Sparus aurata L., Trachurus trachurus L., Mullus barbatus L., Pagrus major T & S., Dicentrarchus labrax L., Mullus surmuletus L. and Oncorhynchus mykiss W. The proposed model (IsVoNet8) is compared with the ResNet50, ResNet101 and VGG16 models. The success accuracies obtained as a result of the comparison are respectively; 98.62% in the IsVoNet8, 91.37% in the ResNet50 model, 86.12% in the ResNet101 model and 97.75% in the VGG16 model. However, it was obtained that the loss rates of ResNet50 0.3646, ResNet101 0.5811, VGG16 0.0696 and with the IsVoNet 0.0568. As a result, it has been observed that the IsVoNet classifies marine fish, which is widely consumed in Türkiye.
Anahtar Kelimeler
Artificial intelligence deep learning convolutional neural network CNN Fish taxonomy
Kaynakça
- Bilecenoglu M, Taskavak E, Mater S & Kaya M (2002). Checklist of the marine fishes of Turkey. Zootaxa 113(1): 1-194
- Colab (2021). Google Colaboratory. Retrieved in November, 24, 2021 from https://colab.research.google.com
- Hridayami P, Putra I K G D & Wibawa K S (2019). Fish species recognition using VGG16 deep convolutional neural network. Journal of Computing Science and Engineering 13(3): 124-130
- Khalifa N E M, Taha M H N & Hassanien A E (2018). Aquarium Family Fish Species Identification System Using Deep Neural Networks, Advances in Intelligent Systems and Computing Proceedings of the International Conference on Advanced Intelligent Systems and Informatics, 1-3 Sep, Cham, pp. 347-356
- Kratzert F & Mader H (2018). Fish species classification in underwater video monitoring using Convolutional Neural Networks, EarthArXiv 15 (5) : 1-8
- Lecun Y, Bengio Y & Hinton G (2015). Deep learning. Nature 521: 436-444
- Majumder A, Rajbongshi A, Rahman M M & Biswas A A (2021). Local freshwater fish recognition using different cnn architectures with transfer learning. International Journal on Advanced Science, Engineering and Information Technology 11(3): 1078-1083
- Mingwang L (2017). Fish Image Recognition and Separation Based on Convolutional Neural Network[J]. Image and Multimedia Technology 3: 82-83
- Montalbo F J P & Hernandez A A (2019). Classification of Fish Species with Augmented Data using Deep Convolutional Neural Network. 2019 IEEE 9th International Conference on System Engineering and Technology (ICSET), 7-7 Oct, Shah Alam, pp. 396-401
- Rathi D, Jain S & Indu S (2017). Underwater fish species classification using convolutional neural network and deep learning. 2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR), 27-30 Dec, Bangalore, pp. 1-6
- Rauf H T, Lali M I U, Zahoor S, Shah S Z H, Rehman A U & Bukhari S A C (2019). Visual features based automated identification of fish species using deep convolutional neural networks. Computers and electronics in agriculture 167: 105075
- Santos A A D & Gonçalves W N (2019). Improving Pantanal fish species recognition through taxonomic ranks in convolutional neural networks. Ecological Informatics 53: 100977
- Sarigül M, & Avci M (2017). Comparison of different deep structures for fish classification. International Journal of Computer Theory and Engineering 9(5): 362-366
- Shah S Z H, Rauf H T, IkramUllah M, Khalid M S, Farooq M, Fatima M & Bukhari S A C (2019). Fish-Pak: Fish species dataset from Pakistan for visual features based classification. Data in brief 27: 104565
- Simonyan K & Zisserman A (2014). Very deep convolutional networks for large-scale image recognition. arXiv: 1409.1556
- Song H A & Lee S Y (2013). Hierarchical representation using NMF. In: International conference on neural information processing, Berlin, Heidelberg, pp. 466–473
- Ulucan O, Karakaya D & Turkan M (2020). A large-scale dataset for fish segmentation and classification. 2020 Innovations in Intelligent Systems and Applications Conference (ASYU), 15-17 Oct, Istanbul, pp. 1-5
- Zhang B, Xie F & Han F (2019). Fish Population Status Detection Based on Deep Learning System. In 2019 IEEE International Conference on Mechatronics and Automation (ICMA), 4-7 Aug, Tianjin, pp. 81-85
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Mühendislik |
| Bölüm | Makaleler |
| Yazarlar | |
| Erken Görünüm Tarihi | 18 Ocak 2023 |
| Yayımlanma Tarihi | 31 Ocak 2023 |
| Gönderilme Tarihi | 1 Aralık 2021 |
| Kabul Tarihi | 25 Mayıs 2022 |
| Yayımlandığı Sayı | Yıl 2023 Cilt: 29 Sayı: 1 |
Kaynak Göster
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Journal of Agricultural Sciences is published as open access journal. All articles are published under the terms of the Creative Commons Attribution License (CC BY).