Key points are not available for this paper at this time.
This paper investigated the integration of LiDAR technology in cut-to-length (CTL) harvesting machines to enhance tree selection accuracy and efficiency. In the evolution of CTL forest machines towards improving operational efficiency and operator conditions, challenges persist in manual tree selection during thinning operations, especially under unmarked conditions and complex environments. These can be improved due to advances in technology. We studied the potential of LiDAR systems in assisting harvester operators, aiming to mitigate workload, reduce decision errors, and optimize the harvesting workflow. We used both synthetic and real-world 3D point cloud data sets for tree stem defect analysis. The former was crafted using a 3D modelling engine, while the latter originated from forest observations using 3D LiDAR on a CTL harvester. Both data sets contained instances of tree stem defects that should be detected. We demonstrated the potential of LiDAR technology: The analysis of synthetic data yielded a Root Mean Square Error (RMSE) of 0.00229 meters (m) and an RMSE percentage of 0.77%, demonstrating high detection accuracy. The real-world data also showed high accuracy, with an RMSE of 0.000767 m and an RMSE percentage of 1.39%. Given these results, we recommend using on-board LiDAR sensor technologies for collecting and analyzing data on tree/forest quality in real-time. This will help overcome existing barriers and drive forest operations toward enhanced efficiency and sustainability.
Sagar et al. (Tue,) studied this question.