AI-Enabled Digital Twins in Agriculture

Digital Twins (DTs) have emerged within the last decade due to the adequate maturity of several key technologies contributing to the realization of real-time virtual–physical world synchronization. Advancements in sensing, connectivity, computing processing power, and artificial intelligence have contributed to the deployment of DTs in several application sectors, such as in agriculture. This work aims to provide a scoping review of recent advancements in digital twin technologies and agricultural applications. Results indicate a special focus on plant-level models, soil moisture, and machinery, while most works are based on drone imagery combined with machine learning routines. Several works use the term DTs rather loosely, often describing systems that resemble decision support tools rather than a fully synchronized virtual–physical setup. Data integration emerges as the most important bottleneck, especially when the system mixes satellite data, local sensory data, and simulation outputs. Yet it is suggested that DTs could eventually support more adaptive and resource-efficient farm management. However, the field is still missing common frameworks and long-term evaluations. Based on this review, progress depends on better data-handling pipelines, clearer definitions of operational DTs, and more attention to economic and practical constraints faced by farmers rather than just technical proofs of concept.