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

Abstract

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 25,000× 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. Note to Practitioners-Monoculture farming is often characterized by heavy agrichemical inputs, such as chemical fertilizers and pesticides, and increased vulnerability to disease and pestilence. This paper is motivated by the lack of long-term sustainability of industrial agriculture, and its implications for human food security. Although polyculture is a sustainable Manuscript

Nitrogen management with reinforcement learning and crop growth models

“…Recently, a few research works have introduced RL for the management of agricultural systems. For instance, RL has been used for climate control in a greenhouse (Wang et al, 2020), planting, and pruning in a polyculture garden (Avigal et al, 2022), fertilizer (Overweg et al, 2021) and/or water management (Chen et al, 2021;Tao et al, 2022;Saikai et al, 2023), coverage path planning (Din et al, 2022), and crop planning (Turchetta et al, 2022) in open-field agriculture. A comprehensive overview of reinforcement learning for crop management support is given in Gautron et al (2022b).…”