IoT based Smart Water Quality Prediction for Biofloc Aquaculture

TL;DR

This paper presents an IoT-based system utilizing sensors, machine learning, and AI to enhance water quality management in biofloc aquaculture, aiming to improve efficiency and productivity.

Contribution

It introduces a novel integrated IoT and AI system specifically designed for biofloc aquaculture water quality prediction and management.

Findings

System successfully collects and analyzes water data

AI-driven decisions improve water quality control

System implementation validated with satisfactory results

Abstract

Traditional fish farming faces several challenges, including water pollution, temperature imbalance, feed, space, cost, etc. Biofloc technology in aquaculture transforms the manual into an advanced system that allows the reuse of unused feed by converting them into microbial protein. The objective of the research is to propose an IoT-based solution to aquaculture that increases efficiency and productivity. The article presented a system that collects data using sensors, analyzes them using a machine learning model, generates decisions with the help of Artificial Intelligence (AI), and sends notifications to the user. The proposed system has been implemented and tested to validate and achieve a satisfactory result.