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Journals / Tarım Bilimleri Dergisi / 2023 / Cilt: 29 - Sayı: 1

IsVoNet8: A Proposed Deep Learning Model for Classification of Some Fish Species

Volkan KAYA
İsmail AKGÜL
Özge ZENCİR TANIR
Pages
298–307
Publication Date
2023-01-31
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

As a result, it has been observed that the IsVoNet classifies marine fish, which is widely consumed in Türkiye.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In the classification of fish, both knowledge and great effort are required to determine the characteristics of fish.
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Key Findings & Data: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.
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Conclusion & Impact:As a result, it has been observed that the IsVoNet classifies marine fish, which is widely consumed in Türkiye.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Artificial intelligence · deep learning · convolutional neural network

Abstract

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 ofcomputer 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 Clupeonellacultriventris 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.

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Cite This Article

KAYA, V., AKGÜL, İ., TANIR, Ö. Z. (2023). IsVoNet8: A Proposed Deep Learning Model for Classification of Some Fish Species. Tarım Bilimleri Dergisi, Cilt: 29 - Sayı: 1, 298–307. https://ajindex.com/articles/880201

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References (13)

  1. Bilecenoglu M, Taskavak E, Mater S & Kaya M (2002). Checklist of the marine fishes of Turkey. Zootaxa 113(1): 1-194
  2. Colab (2021). Google Colaboratory. Retrieved in November, 24, 2021 from https://colab.research.google.com
  3. 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
  4. 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
  5. 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
  6. Lecun Y, Bengio Y & Hinton G (2015). Deep learning. Nature 521: 436-444
  7. 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
  8. 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
  9. Oct, Shah Alam, pp. 396-401
  10. 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
  11. Dec, Bangalore, pp. 1-6
  12. 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
  13. 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