OXYTRACK: AI-Based IOT System for Water Quality Monitoring and Fish Prediction

Sustainable aquaculture production critically depends on continuous monitoring of water quality parameters such as pH, temperature, turbidity, and dissolved oxygen (DO). Conventional manual monitoring techniques are labor-intensive and incapable of providing real-time insights, often leading to delayed intervention and economic losses. This paper presents OxyTrack Pro, a low-cost, AI-enabled Internet of Things (IoT) framework designed for real-time water quality monitoring and intelligent fish suitability prediction. The proposed system integrates multi parameter sensing using an ESP32 microcontroller with cloud-based analytics and a Random Forest regression model for predictive analysis. Sensor data are transmitted via Wi-Fi to a cloud platform and visualized through a mobile dashboard. Experimental evaluation on 1,200 labeled samples demonstrates a prediction accuracy of 95.2%, with low latency (2.6–3.1 s) and reliable alert generation. The proposed framework offers an affordable and scalable solution for smart aquaculture management. Keywords— Artificial Intelligence, Aquaculture Monitoring, ESP32, Fish Prediction, Internet of Things, Water Quality