  This paper presents AlphaGarden: an autonomous polyculture garden that prunes
and irrigates living plants in a 1.5m x 3.0m physical testbed. AlphaGarden uses
an overhead camera and sensors to track the plant distribution and soil
moisture. We model individual plant growth and interplant dynamics to train a
policy that chooses actions to maximize leaf coverage and diversity. For
autonomous pruning, AlphaGarden uses two custom-designed pruning tools and a
trained neural network to detect prune points. We present results for four
60-day garden cycles. Results suggest AlphaGarden can autonomously achieve 0.96
normalized diversity with pruning shears while maintaining an average canopy
coverage of 0.86 during the peak of the cycle. Code, datasets, and supplemental
material can be found at https://github.com/BerkeleyAutomation/AlphaGarden.
