# Optimizing Nitrogen Management with Deep Reinforcement Learning and Crop Simulations

arXiv: https://arxiv.org/abs/2204.10394 · HTML: https://mlsanity.com/paper/2204.10394/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 21 Apr 2022.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Solving Rubik's Cube with a Robot Hand](https://mlsanity.com/paper/1910.07113/) (2019)
- [Learning agile and dynamic motor skills for legged robots](https://mlsanity.com/paper/1901.08652/) (2019)
- [Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising](https://mlsanity.com/paper/1802.09756/) (2018)
- [Autonomous Drone Racing with Deep Reinforcement Learning](https://mlsanity.com/paper/2103.08624/) (2021)
- [Deep Drone Racing: Learning Agile Flight in Dynamic Environments](https://mlsanity.com/paper/1806.08548/) (2018)
- [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, 20 of 20)

- [SwitchTab: Switched Autoencoders Are Effective Tabular Learners](https://mlsanity.com/paper/2401.02013/) (2024)
- [ReConTab: Regularized Contrastive Representation Learning for Tabular Data](https://mlsanity.com/paper/2310.18541/) (2023)
- [MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Training](https://mlsanity.com/paper/2312.08656/) (2023)
- [Residual-based Language Models are Free Boosters for Biomedical Imaging](https://mlsanity.com/paper/2403.17343/) (2024)
- [Adaptive Ensembles of Fine-Tuned Transformers for LLM-Generated Text Detection](https://mlsanity.com/paper/2403.13335/) (2024)
- [Hallucination Improves the Performance of Unsupervised Visual Representation Learning](https://mlsanity.com/paper/2307.12168/) (2023)
- [Extended Agriculture-Vision: An Extension of a Large Aerial Image Dataset for Agricultural Pattern Analysis](https://mlsanity.com/paper/2303.02460/) (2023)
- [Optimizing Crop Management with Reinforcement Learning and Imitation Learning](https://mlsanity.com/paper/2209.09991/) (2022)
- [The New Agronomists: Language Models are Experts in Crop Management](https://mlsanity.com/paper/2403.19839/) (2024)
- [GenCo: An Auxiliary Generator from Contrastive Learning for Enhanced Few-Shot Learning in Remote Sensing](https://mlsanity.com/paper/2307.14612/) (2023)
- [WOFOSTGym: A Crop Simulator for Learning Annual and Perennial Crop Management Strategies](https://mlsanity.com/paper/2502.19308/) (2025)
- [Learning-based agricultural management in partially observable environments subject to climate variability](https://mlsanity.com/paper/2401.01273/) (2024)
- [Current applications and potential future directions of reinforcement learning-based Digital Twins in agriculture](https://mlsanity.com/paper/2406.08854/) (2024)
- [A Comparative Study of Deep Reinforcement Learning for Crop Production Management](https://mlsanity.com/paper/2411.04106/) (2024)
- [Intelligent Agricultural Management Considering N2O Emission and Climate Variability with Uncertainties](https://mlsanity.com/paper/2402.08832/) (2024)
- [Deep Representation Learning for Multi-functional Degradation Modeling of Community-dwelling Aging Population](https://mlsanity.com/paper/2404.05613/) (2024)
- [AgGym: An agricultural biotic stress simulation environment for ultra-precision management planning](https://mlsanity.com/paper/2409.00735/) (2024)
- [Deep Learning Meets Process-Based Models: A Hybrid Approach to Agricultural Challenges](https://mlsanity.com/paper/2504.16141/) (2025)
- [CROPS: A Deployable Crop Management System Over All Possible State Availabilities](https://mlsanity.com/paper/2411.06034/) (2024)
- [Developing and Integrating Trust Modeling into Multi-Objective Reinforcement Learning for Intelligent Agricultural Management](https://mlsanity.com/paper/2505.10803/) (2025)

## Abstract (arXiv)

Nitrogen (N) management is critical to sustain soil fertility and crop production while minimizing the negative environmental impact, but is challenging to optimize. This paper proposes an intelligent N management system using deep reinforcement learning (RL) and crop simulations with Decision Support System for Agrotechnology Transfer (DSSAT). We first formulate the N management problem as an RL problem. We then train management policies with deep Q-network and soft actor-critic algorithms, and the Gym-DSSAT interface that allows for daily interactions between the simulated crop environment and RL agents. According to the experiments on the maize crop in both Iowa and Florida in the US, our RL-trained policies outperform previous empirical methods by achieving higher or similar yield while using less fertilizers