IOT-Based Intelligent Monitoring System for Aquaculture

Abstract

Aquaculture has become an essential contributor to global food security and sustainable protein production. However, many aquaculture environments continue to rely on manual monitoring techniques, which are time-consuming, imprecise, and reactive in nature. This thesis addresses the need for real-time environmental monitoring by developing a data acquisition system based on Internet of Things (IoT) technologies. The system is designed for small to medium-scale freshwater fish farms and focuses on the continuous collection of key water quality parameters, including temperature, pH, and total dissolved solids. Using a Raspberry Pi microcontroller and a set of low-cost sensors, the system enables real-time data capture and remote access, providing fish farmers with more timely and informed decision-making tools. The work begins with an overview of aquaculture practices and the limitations of traditional monitoring methods, followed by a review of existing intelligent aquaculture systems. It then presents the hardware and software setup used for data acquisition and introduces the theoretical framework for applying artificial intelligence (AI), including machine learning (ML) and deep learning (DL) models. These technologies may be used to analyze collected data and support intelligent decision-making within the scope of this thesis. This project lays the groundwork for modern, data-driven aquaculture management by introducing accessible digital monitoring tools and exploring the potential of IoT and AI integration.