Enabling high-throughput quantitative wood anatomy through a dedicated pipeline

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 $\mu$ m, 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 $\mu$ m, 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.

Keywords: Deep learning; Forest ecology; Gigapixel imaging; Image stitching; Increment cores; Quantitative wood anatomy; Robotic sander; Wood discs.

Fig. 1

Robotic sander with working surface and detailed view of fixation of a beech stem disc (upper-right corner)

Fig. 2

The Gigapixel Woodbot (left) and 3D rendering (AutoDesk Fusion 360^®) of the toolhead with laser, camera, ring light and telecentric lens (right)

Fig. 3

Zoom on the same part of a Fagus sylvatica surface, acquired with the Gigapixel woodbot (left and center) and EPSON Perfection V750 Pro (right). The surface was sanded till grit P120 (left) and P4000 (center and right). Images were corrected for brightness and contrast using Fiji [38]

Fig. 4

Top: 24.000 megapixel (24 gigapixel) image of a 30 cm beech disc and two subsequent magnifications. Note the black areas: no images were taken there as informed by the laser height map, which saves time. Bottom: mask of the same disc made by the YOLOv8 model, indicating the vessels (approx. 8.86 million) in blue and the rays in red

Fig. 5

A beech increment core at 2 magnifications. A mask indicating the vessels (blue) and rays (red) is shown under each Gigapixel Woodbot image. A profile of vessel area fraction is plotted below the masks. This profile accounts for varying ring border angles and excludes rays

Fig. 6

Control tab of the Gigapixel Woodbot

Fig. 7

Acquisition tab of the Gigapixel Woodbot

Fig. 8

Analysis tab of the Gigapixel Woodbot

Fig. 9

Calibration tab of the Gigapixel Woodbot

Fig. 10

Flow diagram of acquisition and processing of images

Fig. 11

Domain diagram of the software architecture of the Gigapixel Woodbot. Each rectangle represents a container. The numbers at the arrows represent the communication between containers and are explained in Table 2

Fig. 12

Procedure for imaging of increment cores in a typical sample holder: the laser sensor measures the heights across line 1 and 2 (top) and based on the peaks (bottom, only the results of line 1 are shown) the blue lines (top) are defined along which the laser sensor measures detailed heights to inform the camera the number of images to take at each position

Fig. 13

Performance statistics of the YOLOv8 segmentation model during training. B stands for bounding boxes, M for masks

Fig. 14

Performance statistics of the YOLOv8 segmentation model on the independent test set. Bounding box statistics

Fig. 15

Performance statistics of the YOLOv8 segmentation model on the independent test set. Segmentation mask statistics