Abstract:
Mangrove preservation is crucial due to their ecological significance impact. Monitoring the health of mangrove forests is
essential for preservation strategy, yet it remains challenging and time-intensive, particularly in remote locations. This study aims to
create system to automatically assess mangrove density, providing essential data for informed preservation strategies, such as prioritizing
reforestation in low density area. Using drones with RGB cameras to capture aerial imagery, enabling remote data collection. The system
utilizes the YOLO neural network object detector to automatically detect objects, enabling quantity estimation. Experiment shows that
YOLO object detector is able to detect mangrove tree accurately with 95% recall, 88.3% IoU, and 22ms processing time. The system
uses ’tiny’ model variant to provide more efficient accuracy compared to computation resource, making it suitable for deployment on
computer with limited resources. In comparison to standard model that improves the recall by 4%, IoU by 2%, but demands six times
more processing time. Then it calculate the covered area using camera transformation formula. Finally, it calculates the density for
mangrove forest health, synchronized with GPS location. With the resulting data on mangrove density, evaluations of mangrove forest
health become much easier, facilitating effective preservation actions, such as reforestation in area with low mangrove density.
