Optimizing Tree Diameter Measurement Using RGB-D Cameras: Impacts of Illumination, Camera Elevation, and Sensor-Target Distance in Outdoor Environments

Accurate trunk diameter measurement plays a vital role in oil palm plantation management, supporting applications such as biomass estimation, growth monitoring, and precision agriculture. This study evaluates the performance of a stereo vision-based system for automatic diameter estimation of oil palm trunks. Experiments were conducted under two outdoor lighting conditions: high illumination (9209 lx) and low illumination (2806 lx). The stereo camera was positioned at two different heights (0.55m and 1.1m) and various distances from the trunk (1.0m to 3.0m in 0.5m intervals). The system applies depth segmentation and contour detection techniques to estimate trunk diameter from stereo image data. Results demonstrate that lighting conditions and camera height significantly influence measurement accuracy, with higher illumination levels generally yielding more reliable results. This study shows the potential of stereo vision systems for non-invasive, automated diameter measurement in outdoor environment contributing to more efficient field monitoring and data collection practices.