Smart IoT-Biofloc water management system using Decision regression tree
This addresses water quality issues for fish farmers, but it is incremental as it applies existing machine learning methods to a specific domain.
The paper tackles water management problems in fish farming by proposing an IoT-Biofloc system that uses a Decision regression tree model to predict water conditions, achieving 79% accuracy in experiments.
The conventional fishing industry has several difficulties: water contamination, temperature instability, nutrition, area, expense, etc. In fish farming, Biofloc technology turns traditional farming into a sophisticated infrastructure that enables the utilization of leftover food by turning it into bacterial biomass. The purpose of our study is to propose an intelligent IoT Biofloc system that improves efficiency and production. This article introduced a system that gathers data from sensors, store data in the cloud, analyses it using a machine learning model such as a Decision regression tree model to predict the water condition, and provides real-time monitoring through an android app. The proposed system has achieved a satisfactory accuracy of 79% during the experiment.