# Optimizing Crop Management with Reinforcement Learning and Imitation Learning

arXiv: https://arxiv.org/abs/2209.09991 · HTML: https://mlsanity.com/paper/2209.09991/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 14 of its 54 references that are in the map.
- First posted on arXiv 20 Sep 2022.

## Placed in

- Offline RL and imitation learning from data / [Imitation learning methods](https://mlsanity.com/offlinerl/imitation-learning-methods/)

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Solving Rubik's Cube with a Robot Hand](https://mlsanity.com/paper/1910.07113/) (2019)
- [Sim-to-Real Transfer of Robotic Control with Dynamics Randomization](https://mlsanity.com/paper/1710.06537/) (2017)
- [An Algorithmic Perspective on Imitation Learning](https://mlsanity.com/paper/1811.06711/) (2018)
- [Query-Efficient Imitation Learning for End-to-End Autonomous Driving](https://mlsanity.com/paper/1605.06450/) (2016)
- [Learning Deep Control Policies for Autonomous Aerial Vehicles with MPC-Guided Policy Search](https://mlsanity.com/paper/1509.06791/) (2015)
- [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)
- [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)
- [Machine Learning aided Crop Yield Optimization](https://mlsanity.com/paper/2111.00963/) (2021)
- [Reinforcement Learning under Partial Observability Guided by Learned Environment Models](https://mlsanity.com/paper/2206.11708/) (2022)

## Built on by (most cited first, 13 of 13)

- [ReConTab: Regularized Contrastive Representation Learning for Tabular Data](https://mlsanity.com/paper/2310.18541/) (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)
- [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)
- [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)
- [ChicGrasp: Imitation-Learning based Customized Dual-Jaw Gripper Control for Delicate, Irregular Bio-products Manipulation](https://mlsanity.com/paper/2505.08986/) (2025)
- [Efficient Restarts in Non-Stationary Model-Free Reinforcement Learning](https://mlsanity.com/paper/2510.11933/) (2025)
- [NDRL: Cotton Irrigation and Nitrogen Application with Nested Dual-Agent Reinforcement Learning](https://mlsanity.com/paper/2512.16408/) (2025)

## Abstract (arXiv)

Crop management, including nitrogen (N) fertilization and irrigation management, has a significant impact on the crop yield, economic profit, and the environment. Although management guidelines exist, it is challenging to find the optimal management practices given a specific planting environment and a crop. Previous work used reinforcement learning (RL) and crop simulators to solve the problem, but the trained policies either have limited performance or are not deployable in the real world. In this paper, we present an intelligent crop management system which optimizes the N fertilization and irrigation simultaneously via RL, imitation learning (IL), and crop simulations using the Decision Support System for Agrotechnology Transfer (DSSAT). We first use deep RL, in particular, deep Q-network, to train management policies that require all state information from the simulator as observations (denoted as full observation). We then invoke IL to train management policies that only need a limited amount of state information that can be readily obtained in the real world (denoted as partial observation) by mimicking the actions of the previously RL-trained policies under full observation. We conduct experiments on a case study using maize in Florida and compare trained policies with a maize management guideline in simulations. Our trained policies under both full and partial observations achieve better outcomes, resulting in a higher profit or a similar profit with a smaller environmental impact. Moreover, the partial-observation management policies are directly deployable in the real world as they use readily available information.