  Grapevine winter pruning is a complex task, that requires skilled workers to
execute it correctly. The complexity makes it time consuming. It is an
operation that requires about 80-120 hours per hectare annually, making an
automated robotic system that helps in speeding up the process a crucial tool
in large-size vineyards. We will describe (a) a novel expert annotated dataset
for grapevine segmentation, (b) a state of the art neural network
implementation and (c) generation of pruning points following agronomic rules,
leveraging the simplified structure of the plant. With this approach, we are
able to generate a set of pruning points on the canes, paving the way towards a
correct automation of grapevine winter pruning.
