A Digital Twin-Based Smart Irrigation System Integrating Edge Intelligence and Reinforcement Learning for Sustainable Water Management

Abstract

Efficient irrigation requires decisions that jointly reflect crop water demand, changing weather, soil heterogeneity, actuator
limitations, and uncertainty. This paper proposes a digital twin architecture combining field sensors, edge intelligence, a
calibrated soil–crop water balance, and constrained reinforcement learning. The digital twin maintains a continuously updated
virtual representation of root-zone moisture, crop development, hydraulic state, and short-term meteorological conditions. An
edge controller performs quality checks, state estimation, local inference, safety filtering, and fail-safe actuation even during
network outages. A reinforcement learning agent optimizes irrigation timing and depth subject to soil-moisture, pump-capacity,
and water-allocation constraints. We specify the observation and action spaces, a hybrid physical–data-driven transition model,
a reward function, training protocol, evaluation design, and reproducibility requirements. Numerical tables and plots present
an explicitly synthetic demonstration scenario, not field measurements or independently validated experimental findings. In
this scenario, the proposed policy reduces seasonal applied irrigation relative to a fixed schedule while maintaining an assumed
yield proxy. The contribution is an implementable, auditable design and test protocol rather than a claim of demonstrated
agronomic superiority. Future work must validate transferability across soils, crops, seasons, and sensor failures.