Quantifying solid volume of stacked eucalypt timber using detection-segmentation and diameter distribution models

• Efficient procedure for stacked timber volume quantification. • Automatic log detection and segmentation. • Accurate volume prediction using diameter distribution models. • Scalable solution for complex, large-scale timber stack quantification. Accurate timber quantification is essential in forestry and the timber industry, impacting harvest planning, processing, pricing, and overall supply chain management. Traditional methods for estimating the volume of stacked timber, often reliant on manual measurements, are time-consuming and prone to error. This research aims to develop an accurate procedure for estimating the volume of stacked eucalypt timber in yards. The proposed procedure combines automatic log detection and diameter distribution models. Automatic log detection was achieved using advanced computer vision techniques, specifically the You Only Look Once version 9 (YOLOv9) model, which automates the identification and counting of individual logs within a stack. We used stem diameter distribution models to estimate total stack volume based on log counts and probability densities. This approach ensures high accuracy and efficiency, significantly reducing the time and effort required for volume estimation. The dataset used for this study includes diameter measurements from a pre-harvest inventory of eucalypt trees aged 6 and 7 years, alongside videos of stacked timber. The YOLOv9 model was trained to detect logs from these videos, achieving high precision in object detection and segmentation tasks. Performance metrics such as Box Precision, Box Recall, and mean Average Precision (mAP) were used to evaluate the model's effectiveness. The results indicate that the model generalizes well to new data, with high accuracy in both validation and test sets. Among the distribution models evaluated, the generalized extreme value (GEV) distribution provided the best fit for the stem diameter data, allowing for accurate volume predictions. This procedure, which integrates automatic log detection with diameter distribution models, offers a scalable solution applicable to large and complex timber stacks. Finally, a repository was established to allow users to test the proposed method. Future works will focus on refining the model's accuracy and expanding its applicability across different species, forest production and log conditions.