AI and IoT-Driven Framework for Monitoring and Restoring of Mangrove Ecosystem Health in Coastal Sri Lanka

Abstract

Mangrove ecosystems in Sri Lanka are critical for coastal protection, biodiversity, and carbon sequestration, but face increasing threats from anthropogenic activities and climate change. Traditional monitoring methods often lack the precision and speed required to detect early stage degradation. This study proposes an AI- and IoT-driven framework for monitoring and restoring mangrove ecosystem health in Sri Lanka, based on the global applications of AI, IoT, and Unmanned Aerial Vehicles (UAVs) in mangrove conservation. Findings show that AI techniques such as deep learning, IoT sensors, and UAVs have shown global success in species classification, degradation detection, and high-resolution monitoring. However, Sri Lanka still lacks an integrated AI- and IoT-based system for its mangrove ecosystems. Key gaps include limited localized AI models, poor technological integration, and a lack of real-time monitoring frameworks. To address these gaps, this study proposes a Sri Lanka-specific smart monitoring framework that integrates UAVs for canopy imaging, IoT sensors for root-level data, and AI analytics for real-time anomaly detection and informed decision-making. The four-layered architecture emphasizes data acquisition, secure transmission, machine learning, and an interactive dashboard for real-time monitoring. A phased implementation strategy and recommendations for community engagement, cross-sector partnerships, and policy integration are also provided. With careful piloting and stakeholder collaboration, this framework can transform mangrove conservation efforts in Sri Lanka from reactive to predictive, data-driven ecosystem management.