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

Simulating Polyculture Farming to Tune Automation Policies for Plant Diversity and Precision Irrigation. Yahav Avigal, William Wong, Jensen Gao, Kevin Li, Mark Theis, Mark Preston, Grady Pierroz, Fang Shuo Deng, Ken Goldberg. Winner of Best Student Paper Award. 2020 IEEE Conference on Automation Science and Engineering (CASE), Online (Hong Kong) Aug 20-21, 2020. [Paper] [Presentation Video (15 mins)]

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 than monoculture. As a first step toward developing automation control policies for polyculture farming, we present AlphaGardenSim, a fast, first order, open-access simulator that integrates single plant growth
models with inter-plant dynamics, including light and water competition between plants in close proximity. The simulator approximates growth in a real greenhouse garden at 9,000X the speed of natural growth, allowing for policy parameter tuning. We present an analytic automation policy that in simulation reduced water use and achieved high coverage and plant
diversity compared with other policies, even in the presence of invasive species. Code and supplementary material can be found at https://github.com/BerkeleyAutomation/AlphaGarden.