Simulating Polyculture Farming to Learn Automation Policies for Plant Diversity and Precision Irrigation

Polyculture farming, where multiple crop species are grown simultaneously, has potential to reduce pesticide and water usage while improving the utilization of soil nutrients. However, it is much harder to automate polyculture than monoculture. To facilitate research, we present AlphaGardenSim, a fast, first order, open-access polyculture farming simulator with single plant growth and irrigation models tuned using real world measurements. AlphaGardenSim can be used for policy learning as it simulates inter-plant dynamics, including light and water competition between plants in close proximity and approximates growth in a real greenhouse garden at 25,000x the speed of natural growth. This paper extends earlier work with a new action space that includes planting, which dynamically finds new seed locations that increases resources utilization, and an adaptive sampling technique to reduce the number of actions taken at each timestep without affecting performance. We also evaluate other automation policies using a novel metric that combines plant diversity and canopy coverage. Code and supplementary material can be found at https://github.com/BerkeleyAutomation/AlphaGarden.

Automated summary

Polyculture farming has the potential to reduce pesticide and water usage while improving soil nutrient utilization. However, automating polyculture is more challenging than monoculture. To facilitate research, AlphaGardenSim is introduced as a simulator that can simulate inter-plant dynamics and growth in a greenhouse garden.