Automated Pruning of Polyculture Plants
Mark Presten, Rishi Parikh, Shrey Aeron, Sandeep Mukherjee, Simeon, Adebola, Satvik Sharma, Mark Theis, Walter Teitelbaum, and Ken Goldberg

TL;DR
This paper introduces a novel autonomous system combining hardware and algorithms to automate pruning in polyculture farming, aiming to improve plant diversity and canopy coverage over multiple growth cycles.
Contribution
It presents a new integrated hardware and software system for automated pruning in polyculture, including neural networks and custom tools, tested over 60-day cycles.
Findings
Achieved 0.94 normalized plant diversity with pruning shears
Maintained an average canopy coverage of 0.84
Successfully executed autonomous pruning over four 60-day cycles
Abstract
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…
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Taxonomy
TopicsSmart Agriculture and AI · Greenhouse Technology and Climate Control · Leaf Properties and Growth Measurement
MethodsPruning
