Abstract Background and Aims The shortage of skilled pruners portends automation as an alternative. To train an Artificial Intelligence (AI) system in cane pruning, the attributes of grapevine dormant shoots were characterised. Methods and Results Eight attributes of dormant shoots, diameter, length, vertical and horizontal distance, node number, internode length, origin and position relative to bottom fruiting wire, were measured prior to and after pruning. Vine canopies were modified according to a 5 total node numbers on canes: 10, 20, 30, 40, 50?×?3 total node numbers on spurs: 1, 2, 3 factorial design. Compared to non‐retained dormant shoots, retained dormant shoots were thicker (9.2?±?0.07?mm), longer (104.7?±?0.93?cm), close to the vine trunk (11.4?±?0.65?cm) and below the bottom fruiting wire (83.2?±?0.54?cm). Spurs had the shortest horizontal (9.8?±?0.93?cm) and vertical (77.8?±?0.7?cm) distance, originating from the vine head, base nodes and first nodes of old canes and old spurs. Modifying the vine canopy via increased node numbers had a significant effect on all retained dormant shoot attributes (P?
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