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.

数字孪生在农业中的应用研究综述了以强化学习为基础的数字孪生在农业领域的应用，包括机器人技术、温室管理、灌溉系统和作物管理等，旨在发掘数字孪生与强化学习的整合研究现状，确定未来研究的机会和挑战，并探索协同解决农业问题和优化农业经营的方式，为更高效和可持续的农业方法铺平道路。