Monitoring System for Fish Farming Using Machine Learning

The Monitoring System for Fish Farming Using Machine Learning is a comprehensive solution designed to predict the health status of tilapia fish based on various water quality parameters monitored by IoT sensors. The system leverages machine learning algorithms, including Support Vector Machine (SVM), Random Forest, and XGBoost, to classify fish health into two categories: "Stable" and "At Risk." The dataset used for this project includes vital parameters such as fish weight, survival rate, disease occurrence, temperature, dissolved oxygen, pH levels, turbidity, and oxygenation interventions. The system utilizes these features to assess the health of fish by identifying potential risks based on environmental factors. The back-end of the system is developed using Python with Flask, which processes the input data and provides health predictions. Users can input the relevant parameters on the front-end interface, built using HTML, CSS, and JavaScript, where the system predicts whether the fish are stable or at risk based on the current water quality conditions. The monitoring system provides a significant advancement in the management of fish farming, enabling farmers to make timely decisions regarding interventions like oxygenation and corrective measures

Keywords:

Fish Farming, Machine Learning, IoT, SVM, Random Forest, XGBoost, Water Quality, Health Prediction, Flask, Stable, At Risk.