Environmental Management through Machine Learning-based Fish Species Classification for Sustainable Fisheries
J. Environ. Nanotechnol., Volume 13, No 4 (2024) pp. 232-240
Abstract
Fish species classification is crucial for understanding and preserving marine biodiversity. Advanced technologies such as computer vision and machine learning facilitate the identification and classification of different fish species based on their unique physical characteristics. Automatic fish classification systems are essential for biodiversity assessment, fisheries management, and environmental monitoring. This process involves collecting the image data of fish, extracting relevant features, and training machine learning models. Preprocessing the image data using Gaussian and median filters removes noise and enhances image quality. Mathematical morphological operations are employed for segmentation. For feature extraction, Gray Level Co-occurrence Matrix (GLCM) and geometrical features are used. The GLCM extracts texture features, while geometrical features describe the shape and structure of the fish. Classifiers such as Support Vector Machines (SVM) and Convolutional Neural Networks (CNN) are then used to train the data, comparing it with the extracted features to achieve high accuracy in classification. This accurate classification is critical, especially considering the impact of environmental factors and fish species reduction on the balance of marine ecosystems. Changes in fish population can disrupt the ecological balance, highlighting the importance of effective monitoring and management systems to protect oceanic and sea environments.
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