Application of Machine Learning Techniques in Aquaculture

TL;DR

This paper explores how various machine learning algorithms can be applied to aquaculture by analyzing historical data from farms, yields, and environmental sources to uncover associations and improve practices.

Contribution

It demonstrates the application of multiple machine learning techniques specifically tailored for aquaculture data analysis.

Findings

Identification of key variables affecting aquaculture yields

Improved prediction models for farm management

Enhanced understanding of environmental impacts

Abstract

In this paper we present applications of different machine learning algorithms in aquaculture. Machine learning algorithms learn models from historical data. In aquaculture historical data are obtained from farm practices, yields, and environmental data sources. Associations between these different variables can be obtained by applying machine learning algorithms to historical data. In this paper we present applications of different machine learning algorithms in aquaculture applications.