# A Comparative Study of Deep Reinforcement Learning for Crop Production Management

arXiv: https://arxiv.org/abs/2411.04106 · HTML: https://mlsanity.com/paper/2411.04106/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 6 Nov 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 8 of 8)

- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [CropGym: a Reinforcement Learning Environment for Crop Management](https://mlsanity.com/paper/2104.04326/) (2021)
- [Optimizing Nitrogen Management with Deep Reinforcement Learning and Crop Simulations](https://mlsanity.com/paper/2204.10394/) (2022)
- [gym-DSSAT: a crop model turned into a Reinforcement Learning environment](https://mlsanity.com/paper/2207.03270/) (2022)
- [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)
- [Integrating Reinforcement Learning and Large Language Models for Crop Production Process Management Optimization and Control through A New Knowledge-Based Deep Learning Paradigm](https://mlsanity.com/paper/2410.09680/) (2024)

## Built on by (most cited first, 2 of 2)

- [NDRL: Cotton Irrigation and Nitrogen Application with Nested Dual-Agent Reinforcement Learning](https://mlsanity.com/paper/2512.16408/) (2025)
- [Progressive Generalization Augmentation with Deeply Coupled RND-PPO and Domain-Prioritized Noise Injection for Robust Crop Management Reinforcement Learning](https://mlsanity.com/paper/2605.17428/) (2026)

## Abstract (arXiv)

Crop production management is essential for optimizing yield and minimizing a field's environmental impact to crop fields, yet it remains challenging due to the complex and stochastic processes involved. Recently, researchers have turned to machine learning to address these complexities. Specifically, reinforcement learning (RL), a cutting-edge approach designed to learn optimal decision-making strategies through trial and error in dynamic environments, has emerged as a promising tool for developing adaptive crop management policies. RL models aim to optimize long-term rewards by continuously interacting with the environment, making them well-suited for tackling the uncertainties and variability inherent in crop management. Studies have shown that RL can generate crop management policies that compete with, and even outperform, expert-designed policies within simulation-based crop models. In the gym-DSSAT crop model environment, one of the most widely used simulators for crop management, proximal policy optimization (PPO) and deep Q-networks (DQN) have shown promising results. However, these methods have not yet been systematically evaluated under identical conditions. In this study, we evaluated PPO and DQN against static baseline policies across three different RL tasks, fertilization, irrigation, and mixed management, provided by the gym-DSSAT environment. To ensure a fair comparison, we used consistent default parameters, identical reward functions, and the same environment settings. Our results indicate that PPO outperforms DQN in fertilization and irrigation tasks, while DQN excels in the mixed management task. This comparative analysis provides critical insights into the strengths and limitations of each approach, advancing the development of more effective RL-based crop management strategies.