# A SWAT-based Reinforcement Learning Framework for Crop Management

arXiv: https://arxiv.org/abs/2302.04988 · HTML: https://mlsanity.com/paper/2302.04988/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 33 references that are in the map.
- First posted on arXiv 10 Feb 2023.

## Builds on (most cited first, 5 of 5)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [CropGym: a Reinforcement Learning Environment for Crop Management](https://mlsanity.com/paper/2104.04326/) (2021)
- [Machine Learning aided Crop Yield Optimization](https://mlsanity.com/paper/2111.00963/) (2021)

## Built on by (most cited first, 7 of 7)

- [The New Agronomists: Language Models are Experts in Crop Management](https://mlsanity.com/paper/2403.19839/) (2024)
- [WOFOSTGym: A Crop Simulator for Learning Annual and Perennial Crop Management Strategies](https://mlsanity.com/paper/2502.19308/) (2025)
- [Current applications and potential future directions of reinforcement learning-based Digital Twins in agriculture](https://mlsanity.com/paper/2406.08854/) (2024)
- [To Measure or Not: A Cost-Sensitive, Selective Measuring Environment for Agricultural Management Decisions with Reinforcement Learning](https://mlsanity.com/paper/2501.12823/) (2025)
- [CROPS: A Deployable Crop Management System Over All Possible State Availabilities](https://mlsanity.com/paper/2411.06034/) (2024)
- [Opportunities of Reinforcement Learning in South Africa's Just Transition](https://mlsanity.com/paper/2411.15145/) (2024)
- [Machine Learning in Fish Farming](https://mlsanity.com/paper/2609.13919/) (2026)

## Abstract (arXiv)

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.