Modern agriculture, in its quest for sustainability, resource optimization, and high-quality production under variable climate conditions, requires innovative and scalable solutions. This conceptual work proposes a framework for applying Digital Twin and Cyber-Physical Systems (CPS) to support integrated management across diverse agricultural systems. By combining real-time sensor measurements with manually introduced data, the approach can simulate field conditions, predict optimal interventions, and enhance decision-making. Particular emphasis is placed on supporting traceability, reducing chemical inputs, and enabling more efficient irrigation and fertilization strategies. What sets this framework apart is its adaptability, making it applicable to various crops and farming contexts, and offering a foundation for future applied research and the implementation of smart digital farming practices.
Babanatsas et al. (Thu,) studied this question.