Mangrove Tree Density Detector Using YOLO Based On Darknet Framework

Abstract

Mangroves are important trees that live in swamp areas. Many mangroves are damaged and need preservation. Monitoring mangroves is important for preservation, but it is difficult access by ground and requires a lot of effort and time. There is currently no system specifically to monitor mangrove forest. Therefore this research created mangrove forest density health system. The system gather data using drone to easily gather data from the forest. Then mangrove tree is being detected using YOLO object detector. Experiment shows that YOLO object detector is able to detect mangrove tree accurately with 95% recall, 88.3% IoU, and 22ms processing time. Then the system calculates the density for mangrove forest health. With this system, environmental surveys and monitoring for mangrove forest can be conducted, and resulting density data can be used for effective preservation action.