  Closed-loop irrigation can deliver a promising solution for precision
irrigation. The accurate soil moisture (state) estimation is critical in
implementing the closed-loop irrigation of agrohydrological systems. In
general, the agricultural fields are high dimensional systems. Due to the high
dimensionality for a large field, it is very challenging to solve an
optimizationbased advanced state estimator like moving horizon estimation
(MHE). This work addresses the aforementioned challenge and proposes a
systematic approach for state estimation of large agricultural fields. We use a
non-linear state-space model based on discretization of the cylindrical
coordinate version of Richards equation to describe the agro-hydrological
systems equipped with a central pivot irrigation system. We propose a
structure-preserving adaptive model reduction method using trajectory-based
unsupervised machine learning techniques. Furthermore, the adaptive MHE
algorithm is developed based on an adaptive reduced model. The proposed
algorithms are applied to a small simulated field to compare the performance of
adaptive MHE over original MHE. Finally, the proposed approach is applied to a
large-scale real agricultural field to test the effectiveness and superiority
to address the current challenges. Extensive simulations are carried out to
show the efficiency of the proposed approach.

Systems and Control;Systems and Control