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Dergiler / Journal of Agricultural Sciences / 2026 / Volume: 29 Issue: 1

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

Volkan Kaya(ERZİNCAN BİNALİ YILDIRIM ÜNİVERSİTESİ)0000-0001-6940-3260
İsmail Akgül(ERZİNCAN BİNALİ YILDIRIM ÜNİVERSİTESİ)0000-0003-2689-8675
Özge Zencir Tanır(ERZİNCAN BİNALİ YILDIRIM ÜNİVERSİTESİ)0000-0002-2111-7019
Sayfa
—
Yayın Tarihi
2023-01-31
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Yapay Zeka Hızlı Bakış & Çıkarımlar

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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

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

📌 3 Maddede Bu Makale

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Amaç & Hipotez:In the classification of fish, both knowledge and great effort are required to determine the characteristics of fish.
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Temel Bulgular & Veri: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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Sonuç & Katkı:As a result, it has been observed that the IsVoNet classifies marine fish, which is widely consumed in Türkiye.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler: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 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.

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Bu Makaleye Atıf Yap

Kaya, V., Akgül, İ., Tanır, Ö. Z. (2023). IsVoNet8: A Proposed Deep Learning Model for Classification of Some Fish Species. Journal of Agricultural Sciences, Volume: 29 Issue: 1. https://doi.org/10.15832/ankutbd.1031130

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