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

Abstract

Digital Twins have gained attention in various industries by creating virtual replicas of real-world systems through data collection and machine learning. These replicas are used to run simulations, monitor processes, and support decision-making, extracting valuable information to benefit users. Reinforcement learning is a promising machine learning technique to use in Digital Twins, as it relies on a virtual representation of an environment or system to learn an optimal policy for a given task, which is exactly what a Digital Twin provides. Through its self-learning nature, reinforcement learning can not only optimize given tasks but might also find ways to achieve goals that were previously unexplored and, therefore, open up new avenues to tackle tasks like pest and disease detection, crop growth or crop rotation planning. However, while reinforcement learning can benefit many agricultural practices, the explainability of the employed models is frequently disregarded, diminishing its benefits as users fail to build trust in the suggested decisions. Consequently, there is a notable absence of focus on explainable reinforcement learning techniques, indicating a significant area for future development as an industry as vital to many people as the agri-food sector needs to rely on resilient methods and understandable decisions. Explainable AI models contribute to achieving both of these requirements. Therefore, the use of reinforcement learning in agriculture has the potential to open up a variety of reinforcement learning-based Digital Twin applications in agricultural domains. To explore these domains, This review categorises existing research works that employ reinforcement learning techniques in agricultural settings. On the one hand, we examine the application domain and put them into categories accordingly. On the other hand, we group the works by the reinforcement learning method involved to gain an overview of the currently employed models. Through this analysis, the review seeks to provide insights into the state-of-the-art reinforcement learning applications in agriculture. Additionally, we aim to identify gaps and opportunities for future research focusing on potential synergies of reinforcement learning and Digital Twins to tackle agricultural challenges and optimise farming processes, paving the way for more efficient and sustainable farming methodologies.