DeepFish: A Comprehensive Exploration of Deep Learning Methods for Accurate Fish Species

The DeepFish exploration aims to develop a web-based application that leverages deep learning algorithms to accurately identify and provide detailed information about various fish species based on user-uploaded images, specifically for the unique fish species in Maharashtra. The system utilizes state-of-the-art computer vision techniques to process the uploaded fish images and employs a trained model to predict the fish species. The paper presents a rigorous comparative analysis of deep learning algorithms, to accurately identify the most efficient model for our application with minimum response time and maximum accuracy for identification. The Fish Prediction website provides users with a brief description and relevant information about the predicted fish species including common habitat, diet, physical characteristics, and consumption status, enhancing their knowledge and understanding of the diverse aquatic life. Overall, it provides an accessible and user-friendly platform for fish identification and information retrieval, benefiting researchers, fishing enthusiasts, and conservationists

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Publication Details

Published
2024-08-23
DOI
https://doi.org/10.1109/iccubea61740.2024.10774998
Citations
3
Primary Topic
Identification and Quantification in Food
Type
article
Field-Weighted Citation Impact
0.70
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article

DeepFish: A Comprehensive Exploration of Deep Learning Methods for Accurate Fish Species

Sachin R. Sakhare, Priyanka More, Aarohi Bhand, Rahi Bhand et al.
3 citations
Identification and Quantification in Food
0.70
article

DeepFish: A Comprehensive Exploration of Deep Learning Methods for Accurate Fish Species

Sachin R. Sakhare, Priyanka More, Aarohi Bhand, Rahi Bhand, Pranjal Bharsakale, Swaraj Deshmane, Shreyash Gawande, Vipin Thombare
article en
3 citations

Abstract

The DeepFish exploration aims to develop a web-based application that leverages deep learning algorithms to accurately identify and provide detailed information about various fish species based on user-uploaded images, specifically for the unique fish species in Maharashtra. The system utilizes state-of-the-art computer vision techniques to process the uploaded fish images and employs a trained model to predict the fish species. The paper presents a rigorous comparative analysis of deep learning algorithms, to accurately identify the most efficient model for our application with minimum response time and maximum accuracy for identification. The Fish Prediction website provides users with a brief description and relevant information about the predicted fish species including common habitat, diet, physical characteristics, and consumption status, enhancing their knowledge and understanding of the diverse aquatic life. Overall, it provides an accessible and user-friendly platform for fish identification and information retrieval, benefiting researchers, fishing enthusiasts, and conservationists

International Institute of Information Technology (IN)
Life below water
Openalex Percentile: Top 28%
Identification and Quantification in Food
0.70
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DeepFish: A Comprehensive Exploration of Deep Learning Methods for Accurate Fish Species — Sachin R. Sakhare, Priyanka More, et al. · (2024) | TGRS Research Map | TGRS