# Current applications and potential future directions of reinforcement learning-based Digital Twins in agriculture

arXiv: https://arxiv.org/abs/2406.08854 · HTML: https://mlsanity.com/paper/2406.08854/

## 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 17 of its 92 references that are in the map.
- First posted on arXiv 13 Jun 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, 17 of 17)

- [OmniDrones: An Efficient and Flexible Platform for Reinforcement Learning in Drone Control](https://mlsanity.com/paper/2309.12825/) (2023)
- [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)
- [Position-Agnostic Autonomous Navigation in Vineyards with Deep Reinforcement Learning](https://mlsanity.com/paper/2206.14155/) (2022)
- [A Vision Based Deep Reinforcement Learning Algorithm for UAV Obstacle Avoidance](https://mlsanity.com/paper/2103.06403/) (2021)
- [DQRE-SCnet: A novel hybrid approach for selecting users in Federated Learning with Deep-Q-Reinforcement Learning based on Spectral Clustering](https://mlsanity.com/paper/2111.04105/) (2021)
- [A SWAT-based Reinforcement Learning Framework for Crop Management](https://mlsanity.com/paper/2302.04988/) (2023)
- [Control of rough terrain vehicles using deep reinforcement learning](https://mlsanity.com/paper/2107.01867/) (2021)
- [Robust Model-based Reinforcement Learning for Autonomous Greenhouse Control](https://mlsanity.com/paper/2108.11645/) (2021)
- [Multi-Agent Reinforcement Learning for Unmanned Aerial Vehicle Coordination by Multi-Critic Policy Gradient Optimization](https://mlsanity.com/paper/2012.15472/) (2020)
- [Machine Learning aided Crop Yield Optimization](https://mlsanity.com/paper/2111.00963/) (2021)
- [Reinforcement Learning-Based Deadline and Battery-Aware Offloading in Smart Farm IoT-UAV Networks](https://mlsanity.com/paper/2201.10361/) (2022)
- [Energy Aware Deep Reinforcement Learning Scheduling for Sensors Correlated in Time and Space](https://mlsanity.com/paper/2011.09747/) (2020)
- [A reinforcement learning approach to resource allocation in genomic selection](https://mlsanity.com/paper/2107.10901/) (2021)
- [Deep Reinforcement Learning for Task Offloading in UAV-Aided Smart Farm Networks](https://mlsanity.com/paper/2209.07367/) (2022)
- [IoT-Aerial Base Station Task Offloading with Risk-Sensitive Reinforcement Learning for Smart Agriculture](https://mlsanity.com/paper/2209.07382/) (2022)

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

- [To Measure or Not: A Cost-Sensitive, Selective Measuring Environment for Agricultural Management Decisions with Reinforcement Learning](https://mlsanity.com/paper/2501.12823/) (2025)
- [Advancing site-specific disease and pest management in precision agriculture: From reasoning-driven foundation models to adaptive, feedback-based learning](https://mlsanity.com/paper/2510.24650/) (2025)

## Abstract (arXiv)

Digital Twins have gained attention in various industries for simulation, monitoring, and decision-making, relying on ever-improving machine learning models. However, agricultural Digital Twin implementations are limited compared to other industries. Meanwhile, machine learning, particularly reinforcement learning, has shown potential in agricultural applications like optimizing decision-making, task automation, and resource management. A key aspect of Digital Twins is representing physical assets or systems in a virtual environment, which aligns well with reinforcement learning's need for environment representations to learn the best policy for a task. Reinforcement learning in agriculture can thus enable various Digital Twin applications in agricultural domains. This review aims to categorize existing research employing reinforcement learning in agricultural settings by application domains like robotics, greenhouse management, irrigation systems, and crop management, identifying potential future areas for reinforcement learning-based Digital Twins. It also categorizes the reinforcement learning techniques used, including tabular methods, Deep Q-Networks (DQN), Policy Gradient methods, and Actor-Critic algorithms, to overview currently employed models. The review seeks to provide insights into the state-of-the-art in integrating Digital Twins and reinforcement learning in agriculture, identifying gaps and opportunities for future research, and exploring synergies to tackle agricultural challenges and optimize farming, paving the way for more efficient and sustainable farming methodologies.