The concept of Digital Twins (DTs) has emerged as a transformative technological advancement in precision agriculture, offering unprecedented opportunities for real-time monitoring, predictive analytics, and optimized decision-making in agricultural management. This paper proposes a theoretical model specifically tailored for the implementation of Digital Twin technology in farms located in the Dobrogea region, an area characterized by significant climate variability and resource management challenges. The proposed model integrates multi-layered digital representations of crop conditions, soil properties, climate data, and farm management practices, enabling accurate simulations and predictions. Key components include sensor integration, data analytics platforms, cloud computing solutions, and advanced predictive modeling tools. By establishing virtual replicas of physical agricultural assets, such as fields, machinery, and crop health parameters, the model aims to enhance productivity, improve resource efficiency, and significantly reduce environmental impacts. Additionally, it supports real-time scenario analyses, enabling farmers to proactively identify and address potential issues related to extreme climate events, drought conditions, or limitations in resource availability. Through continuous data synchronization between the digital replicas and their physical counterparts, farmers can rapidly test and evaluate various intervention strategies before actual implementation. The outcomes of this theoretical framework are expected to provide actionable insights into sustainable farm management strategies, clearly demonstrating the value and practical applicability of Digital Twins in the regional agricultural context of Dobrogea.

Digital Twins, precision agriculture, farm management, predictive analytics, sustainability.

agronomy

Presentation: poster