Digital Twins in Agriculture: A State-of-the-art review

highlights•An overview of state-of-the-art research on the topic of Digital Twins in agriculture.•Machine learning enables Digital Twins to be developed for complex agricultural systems, utilizing large amounts of data collected from sensors.•Diverse applications and use-cases were identified, including aquaponic, robotic and greenhouse systems.•Examples of what-if simulation in agricultural Digital Twin applications remain limited The Digital Twin enables the distinctions between state sensing, entity understanding and physical automation to be eliminated, through high-fidelity modelling and bi-directional data streams. The concept of real-time virtual representation places the Digital Twin in a unique position to enable digitization in agriculture. The union of data, modelling and what-if simulation can provide an approach to overcome current limitations in decision-making support and automation, across a diverse range of agricultural enterprises. This paper conducts a Systematic Literature Review of Digital Twins in agriculture, identifying current trends and open questions with the goal of increasing awareness and understanding of the Digital Twin and its possibilities.