@article{01JKAGAK5N15E9RAP0JYRN4K29,
  abstract     = {{Throughout their lifetime, trees store valuable environmental information within their wood. Unlocking this information requires quantitative analysis, in most cases of the surface of wood. The conventional pathway for high-resolution digitization of wood surfaces and segmentation of wood features requires several manual and time consuming steps. We present a semi-automated high-throughput pipeline for sample preparation, gigapixel imaging, and analysis of the anatomy of the end-grain surfaces of discs and increment cores. The pipeline consists of a collaborative robot (Cobot) with sander for surface preparation, a custom-built open-source robot for gigapixel imaging (Gigapixel Woodbot), and a Python routine for deep-learning analysis of gigapixel images. The robotic sander allows to obtain high-quality surfaces with minimal sanding or polishing artefacts. It is designed for precise and consistent sanding and polishing of wood surfaces, revealing detailed wood anatomical structures by applying consecutively finer grits of sandpaper. Multiple samples can be processed autonomously at once. The custom-built open-source Gigapixel Woodbot is a modular imaging system that enables automated scanning of large wood surfaces. The frame of the robot is a CNC (Computer Numerical Control) machine to position a camera above the objects. Images are taken at different focus points, with a small overlap between consecutive images in the X-Y plane, and merged by mosaic stitching, into a gigapixel image. Multiple scans can be initiated through the graphical application, allowing the system to autonomously image several objects and large surfaces. Finally, a Python routine using a trained YOLOv8 deep learning network allows for fully automated analysis of the gigapixel images, here shown as a proof-of-concept for the quantification of vessels and rays on full disc surfaces and increment cores. We present fully digitized beech discs of 30–35 cm diameter at a resolution of 2.25 μm, for which we automatically quantified the number of vessels (up to 13 million) and rays. We showcase the same process for five 30 cm length beech increment cores also digitized at a resolution of 2.25 μm, and generated pith-to-bark profiles of vessel density. This pipeline allows researchers to perform high-detail analysis of anatomical features on large surfaces, test fundamental hypotheses in ecophysiology, ecology, dendroclimatology, and many more with sufficient sample replication.}},
  articleno    = {{11}},
  author       = {{Van den Bulcke, Jan and Verschuren, Louis and De Blaere, Ruben and Vansuyt, Simon and Dekegeleer, Maxime and Kibleur, Pierre and Pieters, Olivier and De Mil, Tom and Hubau, Wannes and Beeckman, Hans and Van Acker, Joris and wyffels, Francis}},
  issn         = {{1746-4811}},
  journal      = {{PLANT METHODS}},
  keywords     = {{Robotic sander,Gigapixel imaging,Deep learning,Increment cores,Wood discs,Forest ecology,Quantitative wood anatomy,Image stitching,TREE-RINGS,CELL,TOOL,CHRONOLOGIES,SYSTEM,DEPTH,FIELD,CBCE}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{24}},
  title        = {{Enabling high-throughput quantitative wood anatomy through a dedicated pipeline}},
  url          = {{http://doi.org/10.1186/s13007-025-01330-7}},
  volume       = {{21}},
  year         = {{2025}},
}

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