Individual Tree Detection using YOLOv8 and Transfer Learning via Drone Data

Effective forest monitoring requires the accurate detection of trees, but traditional methods usually fail due to cost, scale, and precision limitations. This study applies transfer learning using the state-of-the-art YOLOv8 model to improve tree detection from very high-resolution drone imagery. A pre-trained YOLOv8 network was initially trained on the high-resolution VHRTrees satellite imagery dataset and fine-tuned on a smaller very high-resolution drone image dataset. It will resolve issues related to both limited data and domain differences. Experiments explored the impact of spatial resolution at 15 cm and 30 cm, data augmentation, and layer freezing. The results showed that the best accuracy is achieved by fine-tuning using 15 cm resolution while freezing 10 layers; this outperforms models without any transfer learning and those trained from scratch. Data augmentation further reduced false positives, enhancing detection reliability. These findings highlight transfer learning as a cost-effective means to achieve both scalable and precise ecological monitoring-feasible for several applications in forest management and environmental care.