Towards Precise Pruning Points Detection using Semantic-Instance-Aware Plant Models for Grapevine Winter Pruning Automation
This addresses the time-consuming task of grapevine pruning for vineyard workers, but it is incremental as it builds on existing methods for plant modeling and automation.
The paper tackled the problem of automating grapevine winter pruning, which is labor-intensive, by developing a system that detects pruning points using semantic-instance-aware plant models, achieving the generation of pruning points on canes to enable automation.
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.