Automatic resin duct detection and measurement from wood core images using convolutional neural networks
This study introduces a fully automatic CNN-based pipeline for detecting resin ducts and tree‑ring boundaries in pine wood cores collected across Maryland and Virginia. By analyzing over 24,000 ducts and 8,000 ring boundaries, the method greatly improves measurement speed, accuracy, and standardization, offering major advances for dendrochronology and forest ecology.
Subject Tags
- Life Sciences
- Forest
Abstract
The structure and features of resin ducts provide valuable information about environmental conditions accompanying the growth of trees in the genus Pinus. Therefore analysis of resin duct characteristics has been an increasingly common measurement in dendrochronology. However, the measurement is tedious and time-consuming since it requires thousands of ducts to be manually marked in an image of an enlarged wood surface. Although tools exist to automate some stages of this process, no tool exists to automatically recognize and analyze the resin ducts and standardize them with the tree rings they belong to. This study proposes a new fully automatic pipeline that quantifies the properties of resin ducts in terms of the tree ring area to which they belong. A convolutional neural network underlays the pipeline to detect resin ducts and tree-ring boundaries. Also, a region merging procedure is used to identify connected components corresponding to successive rings. Corresponding ducts and rings are next related to each other. The pipeline was tested on 74 wood images representing five Pinus species. Over 8000 tree-ring boundaries and almost 25,000 resin ducts were analyzed. The proposed method detects resin ducts with a sensitivity of 0.85 and precision of 0.76. The corresponding scores for tree-ring boundary detection are 0.92 and 0.99, respectively.
Citations
Fabijańska, A., & Cahalan, G. D. (2023). Automatic resin duct detection and measurement from wood core images using convolutional neural networks. Scientific Reports, 13(1), 7106.
TNC Authors
-
Gabriel Calahan
The Nature Conservancy