  Polyculture farming has environmental advantages but requires substantially
more pruning than monoculture farming. We present novel hardware and algorithms
for automated pruning. Using an overhead camera to collect data from a physical
scale garden testbed, the autonomous system utilizes a learned Plant
Phenotyping convolutional neural network and a Bounding Disk Tracking algorithm
to evaluate the individual plant distribution and estimate the state of the
garden each day. From this garden state, AlphaGardenSim selects plants to
autonomously prune. A trained neural network detects and targets specific prune
points on the plant. Two custom-designed pruning tools, compatible with a
FarmBot gantry system, are experimentally evaluated and execute autonomous cuts
through controlled algorithms. We present results for four 60-day garden
cycles. Results suggest the system can autonomously achieve 0.94 normalized
plant diversity with pruning shears while maintaining an average canopy
coverage of 0.84 by the end of the cycles. For code, videos, and datasets, see
https://sites.google.com/berkeley.edu/pruningpolyculture.
