  Dormant pruning, or the removal of unproductive portions of a tree while a
tree is not actively growing, is an important orchard task to help maintain
yield, requiring years to build expertise. Because of long training periods and
an increasing labor shortage in agricultural jobs, pruning could benefit from
robotic automation. However, to program robots to prune branches, we first need
to understand how pruning decisions are made, and what variables in the
environment (e.g., branch size and thickness) we need to capture. Working
directly with three pruning stakeholders -- horticulturists, growers, and
pruners -- we find that each group of human experts approaches pruning
decision-making differently. To capture this knowledge, we present three
studies and two extracted pruning protocols from field work conducted in
Prosser, Washington in January 2022 and 2023. We interviewed six stakeholders
(two in each group) and observed pruning across three cultivars -- Bing
Cherries, Envy Apples, and Jazz Apples -- and two tree architectures -- Upright
Fruiting Offshoot and V-Trellis. Leveraging participant interviews and video
data, this analysis uses grounded coding to extract pruning terminology,
discover horticultural contexts that influence pruning decisions, and find
implementable pruning heuristics for autonomous systems. The results include a
validated terminology set, which we offer for use by both pruning stakeholders
and roboticists, to communicate general pruning concepts and heuristics. The
results also highlight seven pruning heuristics utilizing this terminology set
that would be relevant for use by future autonomous robot pruning systems, and
characterize three discovered horticultural contexts (i.e., environmental
management, crop-load management, and replacement wood) across all three
cultivars.
