Development of Low-Cost Iot for Tree Instability Detection System and Early Hazard Notification

Abstract

Urban tree failures have emerged as a growing safety issue in Malaysia, with nearly 5,000 reported incidents in 2023 that resulted in fatalities and considerable damage to surrounding infrastructure. Conventional monitoring methods, including manual inspections and GIS-based management systems, remain largely reactive, time-consuming, and costly to maintain. In response to these challenges, this study presents an Internet of Things (IoT)–based early warning system capable of detecting potential tree instability in real time. The system integrates MPU6050 motion sensors with ESP32 microcontrollers and employs LoRa communication technology for long-range, low-power data transmission. Collected data are automatically uploaded to cloud-based Google Sheets for continuous recording and analysis. When the measured tilt angles surpass a defined threshold, immediate alerts are transmitted to responsible personnel through a Telegram Bot interface. This integrated approach provides a practical, low-cost, and scalable solution that improves detection accuracy, reduces reliance on manual observation, and facilitates more proactive management of urban trees. The use of open-source platforms and readily available components also enhances system accessibility, making it suitable for implementation by local authorities and community-based environmental initiatives.