Integrating Machine Learning Models for Accurate Water Quality Assessment in Sustainable Fish Farming

Abstract

Water quality assessment has been a critical factor in aquaculture for centuries, with early methods relying on direct observation and manual testing. Traditional practices included monitoring parameters like temperature, pH, and dissolved oxygen using basic chemical kits and visual inspections. With technological advancements, digital sensors became available, enabling more precise measurements, though they required manual data analysis and interpretation. The objective of this research is to develop an integrated approach that utilizes machine learning models for accurate water quality assessment, promoting sustainable fish farming practices through real-time monitoring and predictive analysis of water parameters. Before machine learning, water quality in fish farming was assessed manually using chemical kits, physical sensors, and visual inspection of water conditions. Traditional water quality assessment methods in fish farming are labor-intensive, time-consuming, and prone to human error, often resulting in delayed responses to changing water conditions, which can adversely impact fish health and productivity. The proposed system involves deploying digital sensors in fish farming setups to continuously collect data on water quality parameters like pH, temperature, and dissolved oxygen. This data is then processed using machine learning models that predict water quality trends, enabling proactive adjustments to maintain optimal conditions for fish growth. An integrated dashboard provides real- time visualization and alerts for timely interventions.