An Integrated Machine Learning System for Realtime Water Quality Monitoring for Seagrass Identification

Abstract

Coastal ecosystems, particularly seagrass meadows, are increasingly threatened by pollution, climate change, and human activities, creating a need for efficient and real-time environmental monitoring systems. This study aimed to develop and evaluate an Integrated Machine Learning System for Realtime Water Quality Monitoring for Seagrass Identification that combines Internet of Things (IoT), machine learning, image processing, and real-time monitoring technologies to support coastal ecosystem management and marine conservation. The study employed a Developmental and Mixed Methods Research Design using the System Development Life Cycle (SDLC) Prototyping Model. The qualitative phase involved stakeholder consultations and expert validation to determine system requirements, usability, and environmental monitoring needs, while the quantitative phase focused on system testing and evaluation using ISO/IEC 25010:2011 software quality standards. The system utilized temperature, pH, and dissolved oxygen sensors integrated with Arduino Uno, Raspberry Pi, GSM communication, and a web-based monitoring dashboard. A Convolutional Neural Network (CNN) was implemented to automate seagrass identification using underwater image analysis. The developed system was evaluated by seventy (70) Marine Biology students and obtained an overall weighted mean of 4.34, verbally interpreted as Very Satisfactory, indicating high acceptability, reliability, and functionality. The study concluded that integrating IoT, machine learning, and environmental sensing technologies can provide a practical and sustainable solution for real-time coastal ecosystem monitoring. It is recommended that future researchers integrate additional water quality parameters, improve AI models through larger datasets, and incorporate mobile and cloud-based technologies to further enhance monitoring accuracy, accessibility, and long-term environmental sustainability.