부산대학교 도서관

The Wallacea region of Sulawesi, Indonesia is renowned for its biodiversity and exceptional endemism. Over the last decade, the region is vulnerable to deforestation, degradation and illegal activities. Frequent monitoring in terms of tree counting provides useful information for various stakeholders such as forest management, government institutions, and environmental agencies. Existing monitoring methods include labour intensive manual observations and satellite imaging remote sensing technology. Satellite-based imagery is low resolution, infrequent, and sometimes include cloud cover. To overcome these drawbacks, this research utilises UAV-based high-resolution RGB images processed by machine learning algorithm to detect tree species, i.e., Sugarpalm, Clove, and Coconut. We compared many deep learning algorithms and found that YOLOv5 model is lightweight, easy to use, fast and accurate for tree species identification.