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Key Message <p>The automatic detection of tree-ring boundaries and other anatomical features using image analysis has progressed substantially over the past decade with advances in machine learning and imagery technology, as well as increasing demands from the dendrochronology community. This paper presents a publicly available dataset of 64 annotated images of transverse sections of commercially grown <i>Pinus taeda </i>L. trees from northern Uruguay, presenting 17 to 24 annual rings. The collection contains several challenging features for automatic ring detection, including illumination and surface preparation variation, fungal infection (blue stains), knot formation, missing bark or interruptions in outer rings, and radial cracking. This dataset can be used to develop and test automatic tree ring detection algorithms. The dataset presented here was used to develop the Cross-Section Tree-Ring Detection (CS-TRD) method, an open-source automated ring-detection algorithm for cross-sectioned images. Dataset access at <a href="https://doi.org/10.5281/zenodo.15110647">https://doi.org/10.5281/zenodo.15110647</a>.&#xa0;Access to the metadata describing the data set:&#xa0;<a href="https://metadata-afs.nancy.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/5fdbd411-9ae1-4ce6-8ef0-cdfa2fbd7a6a">https://metadata-afs.nancy.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/5fdbd411-9ae1-4ce6-8ef0-cdfa2fbd7a6a</a>.</p>

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UruDendro, a public dataset of 64 cross-section images and manual annual ring delineations of Pinus taeda L.

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月1日

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  • Henry Marichal,
  • Diego Passarella,
  • Christine Lucas,
  • Ludmila Profumo,
  • Verónica Casaravilla,
  • María Noel Rocha Galli,
  • Serrana Ambite,
  • Gregory Randall

摘要

Key Message

The automatic detection of tree-ring boundaries and other anatomical features using image analysis has progressed substantially over the past decade with advances in machine learning and imagery technology, as well as increasing demands from the dendrochronology community. This paper presents a publicly available dataset of 64 annotated images of transverse sections of commercially grown Pinus taeda L. trees from northern Uruguay, presenting 17 to 24 annual rings. The collection contains several challenging features for automatic ring detection, including illumination and surface preparation variation, fungal infection (blue stains), knot formation, missing bark or interruptions in outer rings, and radial cracking. This dataset can be used to develop and test automatic tree ring detection algorithms. The dataset presented here was used to develop the Cross-Section Tree-Ring Detection (CS-TRD) method, an open-source automated ring-detection algorithm for cross-sectioned images. Dataset access at https://doi.org/10.5281/zenodo.15110647. Access to the metadata describing the data set: https://metadata-afs.nancy.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/5fdbd411-9ae1-4ce6-8ef0-cdfa2fbd7a6a.