A SWAT-based Reinforcement Learning Framework for Crop Management
- URL: http://arxiv.org/abs/2302.04988v1
- Date: Fri, 10 Feb 2023 00:24:22 GMT
- Title: A SWAT-based Reinforcement Learning Framework for Crop Management
- Authors: Malvern Madondo, Muneeza Azmat, Kelsey Dipietro, Raya Horesh, Michael
Jacobs, Arun Bawa, Raghavan Srinivasan, Fearghal O'Donncha
- Abstract summary: We introduce a reinforcement learning (RL) environment that leverages the dynamics in the Soil and Water Assessment Tool (SWAT)
This drastically saves time and resources that would have been otherwise deployed during a full-growing season.
We demonstrate the utility of our framework by developing and benchmarking various decision-making agents following management strategies informed by standard farming practices and state-of-the-art RL algorithms.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Crop management involves a series of critical, interdependent decisions or
actions in a complex and highly uncertain environment, which exhibit distinct
spatial and temporal variations. Managing resource inputs such as fertilizer
and irrigation in the face of climate change, dwindling supply, and soaring
prices is nothing short of a Herculean task. The ability of machine learning to
efficiently interrogate complex, nonlinear, and high-dimensional datasets can
revolutionize decision-making in agriculture. In this paper, we introduce a
reinforcement learning (RL) environment that leverages the dynamics in the Soil
and Water Assessment Tool (SWAT) and enables management practices to be
assessed and evaluated on a watershed level. This drastically saves time and
resources that would have been otherwise deployed during a full-growing season.
We consider crop management as an optimization problem where the objective is
to produce higher crop yield while minimizing the use of external farming
inputs (specifically, fertilizer and irrigation amounts). The problem is
naturally subject to environmental factors such as precipitation, solar
radiation, temperature, and soil water content. We demonstrate the utility of
our framework by developing and benchmarking various decision-making agents
following management strategies informed by standard farming practices and
state-of-the-art RL algorithms.
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