Automated Pruning and Irrigation of Polyculture Plants

Polyculture farming has environmental advantages but requires substantially more labor than monoculture farming. We present novel hardware and algorithms for automated pruning and irrigation. Using an overhead camera to collect data from physical <inline-formula> <tex-math notation="LaTeX">$1.5 m^{2}$ </tex-math></inline-formula> garden testbeds, 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 commercial gantry system, are experimentally evaluated. Irrigation is automated using soil moisture sensors. We present results for four 60-day garden cycles. Results suggest the system can autonomously achieve 94% normalized plant diversity with pruning shears while maintaining an average canopy coverage of 84% by the end of the cycles. For code, videos, and datasets, see <uri>https://sites.google.com/berkeley.edu/pruningpolyculturej/home</uri>. Note to Practitioners—While polyculture farming is closer to how plants grow in nature, it is considered more labor intensive that monoculture farming. In this paper we present approaches and custom hardware for automation of pruning and irrigation. Physical experiments suggest that automation can yield both high coverage and diversity.