This repo contains the R scripts to detect whorls from dense drone laser scanning point clouds.
The full methodology is described in:
Puliti, S., McLean, J.P., Cattaneo, N., Fischer, C., Astrup, R. 2022. Tree height-growth trajectory estimation using uni-temporal UAV laser scanning data and deep learning. Forestry volume .... pp .... DOI: .....
In this repo you will be able to process a single tree according to the methodology developed by Puliti et al. 2022. The code is mainly based on R but calls python for the YOLO inference. Thus python should be installed on the machine.
git clone https://github.com/stefp/YOLOv5-whorlDetectorThe model can be dowloaded from this link (https://drive.google.com/file/d/1_kNcQrUuSiYxvjItw_nBJWiRq9YIdR0D/view?usp=sharing) and should be stored in the .../YOLOv5-whorlDetector/src/whorl_detector_weights folder