mlsanityData to 14 Oct 2026

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

arXiv 2406.08854 ↗First posted 13 Jun 2024cs.LG cs.AIcited by 2

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

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.

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.

Builds on

17 of 92 references.

References · 17

  1. 2023OmniDrones: An Efficient and Flexible Platform for Reinforcement Learning in Drone Controlcited by 33
  2. 2021CropGym: a Reinforcement Learning Environment for Crop Managementcited by 21
  3. 2022Optimizing Nitrogen Management with Deep Reinforcement Learning and Crop Simulationscited by 20
  4. 2022gym-DSSAT: a crop model turned into a Reinforcement Learning environmentcited by 13
  5. 2022Position-Agnostic Autonomous Navigation in Vineyards with Deep Reinforcement Learningcited by 11
  6. 2021A Vision Based Deep Reinforcement Learning Algorithm for UAV Obstacle Avoidancecited by 10
  7. 2021DQRE-SCnet: A novel hybrid approach for selecting users in Federated Learning with Deep-Q-Reinforcement Learning based on Spectral Clusteringcited by 7
  8. 2023A SWAT-based Reinforcement Learning Framework for Crop Managementcited by 7
  9. 2021Control of rough terrain vehicles using deep reinforcement learningcited by 6
  10. 2021Robust Model-based Reinforcement Learning for Autonomous Greenhouse Controlcited by 6
  11. 2020Multi-Agent Reinforcement Learning for Unmanned Aerial Vehicle Coordination by Multi-Critic Policy Gradient Optimizationcited by 5
  12. 2021Machine Learning aided Crop Yield Optimizationcited by 5
5 more
  1. 2022Reinforcement Learning-Based Deadline and Battery-Aware Offloading in Smart Farm IoT-UAV Networkscited by 4
  2. 2020Energy Aware Deep Reinforcement Learning Scheduling for Sensors Correlated in Time and Spacecited by 3
  3. 2021A reinforcement learning approach to resource allocation in genomic selectioncited by 3
  4. 2022Deep Reinforcement Learning for Task Offloading in UAV-Aided Smart Farm Networkscited by 2
  5. 2022IoT-Aerial Base Station Task Offloading with Risk-Sensitive Reinforcement Learning for Smart Agriculturecited by 2

Built on by

Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025To Measure or Not: A Cost-Sensitive, Selective Measuring Environment for Agricultural Management Decisions with Reinforcement Learningcited by 2
  2. 2025Advancing site-specific disease and pest management in precision agriculture: From reasoning-driven foundation models to adaptive, feedback-based learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2022Optimizing Crop Management with Reinforcement Learning and Imitation Learning
  2. 2024A Comparative Study of Deep Reinforcement Learning for Crop Production Management
  3. 2024The New Agronomists: Language Models are Experts in Crop Management
  4. 2026Optimizing Path Planning using Deep Reinforcement Learning for UGVs in Precision Agriculture
  5. 2024Comparative Analysis of Multi-Agent Reinforcement Learning Policies for Crop Planning Decision Support
  6. 2024Integrating Reinforcement Learning and Large Language Models for Crop Production Process Management Optimization and Control through A New Knowledge-Based Deep Learning Paradigm
  7. 2025Improving Q-Learning for Real-World Control: A Case Study in Series Hybrid Agricultural Tractors
  8. 2024Learning-based agricultural management in partially observable environments subject to climate variability