Impact of Climate Change on Fish Species Classification Using Machine Learning and Deep Learning Algorithms

Abstract

In response to the challenges posed by climate change and the need for sustainable food supply, this study addresses the problem of efficiently categorizing and predicting the weight of fish in aquaculture. Leveraging machine learning and deep learning algorithms, we propose a regression model to predict fish weight and classification models for species identification based on weight, width, and length parameters. The focus is on automating fish farming processes to ensure uninterrupted food supply amidst environmental uncertainties. Comparative analysis of various machine learning algorithms reveals promising accuracy levels, with deep learning sequential models achieving 99.77% accuracy under specific conditions. This research aims to contribute to the advancement of automated fish farming practices, mitigating the impact of climate change on food security and promoting sustainable resource management.