Real-Time Aquatic Biodiversity Monitoring through IoT and Machine Learning Integration.

Abstract

The integration of the Internet of Things (IoT) with machine learning (ML) has emerged as a transformative approach for environmental monitoring, particularly in aquatic ecosystems where biodiversity assessment requires high temporal and spatial resolution. This study explores an IoT-enabled, machine-learning-based framework for real-time monitoring of aquatic biodiversity, emphasizing the detection and analysis of biotic indicators such as plankton, fish populations, and water quality parameters. The system employs a network of smart sensors measuring temperature, pH, dissolved oxygen, turbidity, and nitrate concentration, transmitting continuous data through low-power wide-area networks (LPWANs) to a cloud-based analytics platform. Machine learning algorithms, including Random Forest and Convolutional Neural Networks (CNNs), are utilized to identify patterns, anomalies, and species distribution trends. Field implementation was conducted across selected freshwater lakes and estuaries in South India, integrating both in-situ sensor data and satellite imagery for validation. Results demonstrate a significant improvement in accuracy and responsiveness over conventional manual sampling, reducing detection latency and enhancing ecological forecasting. The proposed architecture provides a scalable and cost-efficient solution for environmental agencies, policymakers, and conservationists, enabling proactive biodiversity management and early warning against habitat degradation and species loss through continuous, intelligent observation of aquatic ecosystems.