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.