Towards automated grape vine pruning: Learning by example using recurrent graph neural networks

Abstract

Vine pruning in vineyards is an important canopy management activity. It requires skill and experience to perform well and poor pruning results in low yield and can have a long-term effect on the productivity of the vine. Automated systems usually rely on simplified pruning rules that need to be specified before operation and largely prune all vines in the same way. This is not a realistic approach and limits the pruning quality. We propose a step toward an automated system that can learn pruning behavior from expert examples without the need for explicit pruning rules. By using a novel neural network architecture we train a classifier that can choose which canes should be pruned and which should be kept. We show that the classifier can learn simple pruning rules without prior knowledge and show how this can be used as part of an automated system. The system is robust to vines that are more complex and diverse than those it was trained on and shows potential for future models to be pretrained using synthetic data and fine tuned on a minimal set of expertly pruned vines.