Development of a Jetson Nano drone for monitoring and detection of forest fires
DOI:
https://doi.org/10.53591/easi.v4i2.2674Abstract
This project presents the development of an artificial intelligence-based prototype for the monitoring and detection of forest fires. The prototype consists of a drone equipped with a high-resolution camera connected to a Jetson Nano. The system was programmed in Python to develop Convolutional Neural Network models for identifying fire indicators, such as smoke and flames, from the images captured by the drone. Unlike traditional methods that rely on fixed or satellite sensors or cameras, this mobile approach enables flexible surveillance of large forest areas, even in hard-to-reach terrain. During testing, an accuracy rate of 95% to 99% was achieved, demonstrating the system’s capability to process large volumes of visual data in real time. The maximum video transmission range is approximately 3.7 km without interference. Smoke and fire detections were transmitted to the ThingSpeak platform allowing visualization and analysis to facilitate fast and effective decision-making. It is concluded that the prototype aims to enhance response capabilities by enabling early fire and minimizing environmental and economic damage.
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