AI-Enabled Iot System for Real-Time Green Roof Microclimate Monitoring and Optimization in Sri Lanka

AI-Enabled IoT-Based Real-Time Green Roof Microclimate Monitoring and Optimization System for Tropical Environments. This research presents the design and implementation of an AI-enabled IoT-based real-time green roof microclimate monitoring and optimization system for tropical environments in Sri Lanka. The proposed system addresses key challenges such as urban heat stress, inefficient water usage, and limited real-time monitoring of green infrastructure systems. The system is built using an ESP32 microcontroller and integrates IoT sensors, including the AHT20 temperature and humidity sensor, soil moisture sensor, and BH1750 light intensity sensor. These sensors continuously measure critical microclimate parameters such as temperature, humidity, soil moisture, and light intensity. The collected data is transmitted wirelessly to a cloud-based platform for storage, processing, and visualization through a web-based dashboard. An AI-based analysis module is developed to process environmental data patterns and support intelligent decision-making. The system generates irrigation recommendations by analyzing changes in soil moisture and environmental conditions to improve water efficiency and maintain optimal plant health. In addition, real-time alerts are provided when environmental parameters exceed predefined thresholds, enabling timely intervention. Experimental results show that the proposed system can effectively monitor microclimate variations and support automated irrigation decision-making. The integration of IoT sensing and AI-based analysis provides a scalable, low-cost, and energy-efficient solution for smart green roof management in tropical urban environments.