Towards Sustainable Aquaculture: IoT and ML Approaches for Water Quality Monitoring

Abstract

Aquaculture that is run in a sustainable manner is essential to meeting the ever-increasing demand for seafood throughout the world while also reducing the environmental impact of seafood consumption. The purpose of this work is to investigate the use of Internet of Things (IoT) and Machine Learning (ML) methodologies in aquaculture systems for the purpose of monitoring water quality. We investigate the potentially revolutionary effects of these technologies by reading a variety of research articles and analysing their findings. Aquaculturists are given quick insights into water quality factors like as temperature, pH, and dissolved oxygen thanks to the ability of IoT sensors to capture data in real time. Processing this data, providing early warning systems for disease outbreaks, optimising resource utilisation, and guaranteeing regulatory compliance are all very important roles that machine learning algorithms play. These advances not only make aquaculture operations more efficient but also help contribute to the industry's overall sustainability. They contribute to the reduction of resource consumption, the mitigation of adverse effects on the environment, and the promotion of responsible aquaculture practises. The research that was looked through highlights how important it is for IoT and ML to play a part in bringing aquaculture into the future in a way that is more ecologically friendly and sustainable. The aquaculture sector is continuously undergoing change, and adopting these new technologies is very necessary in order to strike a healthy balance between the production of seafood and the maintenance of ecosystems.