Internal checking and shrinkage are drying defects that strongly degrade timber for structural and appearance uses in many hardwood species. Wood quality assessors and tree breeders often measure checks and shrinkage from disc-derived wood wedges using scale-based methods and callipers, but these methods are subjective and labour intensive. This study developed an R-based open-source system using image thresholding techniques to quantify checks and shrinkage from digital images of wood wedges. The results showed that the automated quantification of checks predicted the subjective and manual assessment for both area and number of checks in Eucalyptus nitens at the wedge level, and provided much more precision than the subjective classification of checking used by breeders for little additional effort. Similarly, the automated image assessment explained a high proportion of the manually measured variation in shrinkage and collapse with little bias. The automated assessment resulted in significant time saving compared with the manual measurements from digital images. The R-based image analysis thus shows promise in replacing traditional assessment when evaluating a large number of samples and quantitative estimates of checks and shrinkage are required, and has the added advantage that the distribution of checks and collapse within the wedges can be obtained to assist diverse studies on drying defects.

内部检查和收缩是干燥的缺陷，这些缺陷会严重降解木材，从而在许多硬木树种中用作结构和外观。木材质量评估员和树木育种者通常使用基于比例的方法和游标卡尺来测量圆盘衍生的木楔的检查和收缩，但是这些方法是主观的且劳动强度大的。这项研究开发了一种基于R的开源系统，该系统使用图像阈值技术来量化木楔的数字图像中的检查和收缩。结果表明，检查的自动量化预测了桉树的检查面积和检查次数的主观和人工评估在楔形层次上，它提供了比种鸽主观检查分类更高的精度，而无需付出额外的努力。类似地，自动图像评估解释了手动测量的收缩和塌陷变化的很大一部分，偏差很小。与通过数字图像进行手动测量相比，自动评估可以节省大量时间。因此，基于R的图像分析显示出在评估大量样品和要求定量检查和收缩的情况下取代传统评估的潜力，并具有额外的优势，即可以在楔形内获得检查和塌陷的分布，从而有助于关于干燥缺陷的各种研究。