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Fish Species Recognition using Convolutional Neural Networks for Biodiversity Monitoring
Subject area: Science,Engineering and Technology · Area of research: Machine Learning, Deep Learning
Abstract
In this research, our goal is to develop an automated system using Convolutional Neural Networks (CNNs) for recognizing fish species, thereby enhancing biodiversity monitoring efficiency. We focus on three specific fish species?Brachaluteres jacksonianus, Cantherhines dumerilii, and an additional species?and begin by curating a diverse dataset of high-resolution images from various aquatic environments. Utilizing the Python Imaging Library (PIL) and other open-source libraries, we preprocess, explore, and visually represent the dataset. The essence of our study lies in training a CNN model to accurately classify fish species based on their unique visual features. The model undergoes meticulous training and validation with a carefully divided dataset, emphasizing high accuracy and generalization. Visualizations of training history, encompassing accuracy and loss metrics, enable comprehensive evaluation across multiple epochs. Our proposed CNN model shows promise in revolutionizing biodiversity monitoring, offering a scalable and automated solution for precise fish species recognition. Successful implementation could streamline data collection, enhance ecological study efficiency, and provide critical insights for sustainable aquatic ecosystem management, marking a significant stride in integrating technology into conservation practices.
Keywords
Fish species recognition, Convolutional Neural Networks (CNN), Biodiversity monitoring, Automated classification, Ecological sustainability.
How to cite this paper
@article{1705306,
author = {Amit Kumar Pandey, Dr. Santosh Singh, Ankush Sushil Singh, Shravan Shivanand Kamat},
title = {Fish Species Recognition using Convolutional Neural Networks for Biodiversity Monitoring},
journal = {Iconic Research And Engineering Journals},
year = {2023},
volume = {7},
number = {6},
pages = {223-227},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1705306.pdf},
abstract = {In this research, our goal is to develop an automated system using Convolutional Neural Networks (CNNs) for recognizing fish species, thereby enhancing biodiversity monitoring efficiency. We focus on three specific fish species?Brachaluteres jacksonianus, Cantherhines dumerilii, and an additional species?and begin by curating a diverse dataset of high-resolution images from various aquatic environments. Utilizing the Python Imaging Library (PIL) and other open-source libraries, we preprocess, explore, and visually represent the dataset. The essence of our study lies in training a CNN model to accurately classify fish species based on their unique visual features. The model undergoes meticulous training and validation with a carefully divided dataset, emphasizing high accuracy and generalization. Visualizations of training history, encompassing accuracy and loss metrics, enable comprehensive evaluation across multiple epochs. Our proposed CNN model shows promise in revolutionizing biodiversity monitoring, offering a scalable and automated solution for precise fish species recognition. Successful implementation could streamline data collection, enhance ecological study efficiency, and provide critical insights for sustainable aquatic ecosystem management, marking a significant stride in integrating technology into conservation practices.},
keywords = {Fish species recognition, Convolutional Neural Networks (CNN), Biodiversity monitoring, Automated classification, Ecological sustainability.},
month = {December},
}