UruDendro4: A Benchmark Dataset for Automatic Tree-Ring Detection in Cross-Section Images of Pinus taeda L
This addresses the time-consuming and imprecise manual measurement of tree-ring growth for forestry researchers, though it is incremental as it builds on existing automated methods with a new dataset.
The authors tackled the problem of automating tree-ring detection in cross-sectional images by introducing the UruDendro4 dataset of 102 annotated Pinus taeda L. samples, achieving a baseline performance with DeepCS-TRD method at 0.838 mean Average Precision and 0.782 mean Average Recall.
Tree-ring growth represents the annual wood increment for a tree, and quantifying it allows researchers to assess which silvicultural practices are best suited for each species. Manual measurement of this growth is time-consuming and often imprecise, as it is typically performed along 4 to 8 radial directions on a cross-sectional disc. In recent years, automated algorithms and datasets have emerged to enhance accuracy and automate the delineation of annual rings in cross-sectional images. To address the scarcity of wood cross-section data, we introduce the UruDendro4 dataset, a collection of 102 image samples of Pinus taeda L., each manually annotated with annual growth rings. Unlike existing public datasets, UruDendro4 includes samples extracted at multiple heights along the stem, allowing for the volumetric modeling of annual growth using manually delineated rings. This dataset (images and annotations) allows the development of volumetric models for annual wood estimation based on cross-sectional imagery. Additionally, we provide a performance baseline for automatic ring detection on this dataset using state-of-the-art methods. The highest performance was achieved by the DeepCS-TRD method, with a mean Average Precision of 0.838, a mean Average Recall of 0.782, and an Adapted Rand Error score of 0.084. A series of ablation experiments were conducted to empirically validate the final parameter configuration. Furthermore, we empirically demonstrate that training a learning model including this dataset improves the model's generalization in the tree-ring detection task.