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

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 publ...

Ամբողջական նկարագրություն

Պահպանված է:
Հիմնական հեղինակ: Marichal, Henry (author)
Այլ հեղինակներ: Passarella, Diego (author), Lucas, Christine (author), Profumo, Ludmila (author), Casaravilla, Verónica (author), Rocha Galli, María Noel (author), Ambite, Serrana (author), Randall, Gregory (author)
Ձևաչափ: article
Լեզու:անգլերեն
Հրապարակվել է: 2025
Խորագրեր:
Image processing
Tree ring area
Tree ring width
Wood cross sections
Dendrometry
Automatic measurement
Առցանց հասանելիություն:https://annforsci.biomedcentral.com/articles/10.1186/s13595-025-01296-5#citeas
https://hdl.handle.net/20.500.12008/50482
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Ամփոփում: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. 15110 647. Access to the metadata describing the data set : https:// metad ata- afs. nancy. inra. fr/ geone twork/ srv/ fre/ catal og. searc h#/ metad ata/ 5fdbd 411- 9ae1- 4ce6- 8ef0- cdfa2 fbd7a 6a.

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