Abstract: The knowledge of the species of different fishes is of utmost importance in fisheries management, which involves monitoring fish population and their habitats to ensure sustainable fishing practices. Over the years, the knowledge regarding the species of fishes was not known widely by the people and it most depended on the human observer. But in recent years, the development of machine learning and deep learning has enabled us with a powerful tool to help ease those problems. These systems can accurately detect the fish in the images and provide us with the name of the species of fish in the image. This paper presents a comprehensive review of the fish prediction technologies and systems. This paper also introduces an approach of using deep learning and convolutional neural network(CNN) to predict the species of the fish. The proposed approach consists of three stages: data pre-processing, feature extraction and classification. The data pre-processing stage involves preparing the raw image by applying various filters and transformations. The feature extraction stage involves using pre trained CNN models to extract relevant features from the images. Finally, the classification stage involves using a support vector machine (SVM) classifier the extracted features as fish or non-fish. This paper also discusses the potential application of the proposed fish prediction methods, including fish management, environmental monitoring, and scientific research. Furthermore, this paper highlights the limitations of the fish prediction system and also discusses the future applications and scope of it.
Overall, this paper gives a comprehensive overview of fish species prediction techniques and presents an approach to fish species prediction using deep learning and CNNs. The proposed method has the potential to significantly improve the fish conservation, and contribute to the sustainability of fish population and marine ecosystem.
Keywords: CNN(Convolutional Neural Network), Data Pre-Processing, Feature Extraction, SVM(Support Vector Machine)
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DOI:
10.17148/IJARCCE.2024.134201
