Researcher profile

Diego Passarella

Diego Passarella contributes to research discovery and scholarly infrastructure.

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Published work

2 published item(s)

preprint2026arXiv

TRAS: An Interactive Software for Tracing Tree Ring Cross Sections

Tree ring marking remains a key step in dendrometry and dendrochronology, but it is often performed manually, making the process time-consuming, subjective, and difficult to scale to large image datasets. We present the Tree Ring Analyzer Suite (TRAS), an open-source graphical software for automatic delineation, manual correction, and measurement of tree rings in wood cross-sectional images. TRAS integrates three complementary detection algorithms: the classical image-processing method CS-TRD and two deep-learning approaches, DeepCS-TRD and INBD. The interface allows users to refine automatic detections, remove false positives, and manually add missing rings. It also computes dendrochronological metrics such as earlywood and latewood areas, ring perimeter, equivalent ring width, and custom path-based ring-width measurements. TRAS was evaluated on 18 expertly annotated Pinus taeda L. cross-section images. DeepCS-TRD achieved the best automatic detection performance, with an F-score of 81.0% and precision of 86.4%. Automatic detection reduced the required manual correction effort to approximately 20% of ring boundaries. For one-dimensional ring-width measurements, TRAS showed excellent agreement with CooRecorder ($r > 0.99$). Common detection errors, such as jump propagation or false positives near knots, were easily corrected through the postprocessing interface. TRAS provides a flexible and reproducible solution for tree-ring analysis on Windows, macOS, and Linux. Code is available at the https://hmarichal93.github.io/tras.

preprint2025arXiv

UruDendro4: A Benchmark Dataset for Automatic Tree-Ring Detection in Cross-Section Images of Pinus taeda L

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.