Adaptive Model Reduction and State Estimation of Agro-hydrological Systems

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• An adaptive model reduction approach for agro-hydrological systems. • An adaptive state estimation approach based on the adaptive model reduction. • Extensive simulations with real field data showing the efficiency of the proposed approaches. Closed-loop irrigation can deliver a promising solution for precision irrigation. The accurate soil moisture (state) estimation is critical in implementing a closed-loop agricultural irrigation system. The water dynamics in an agro-hydrological system may be modeled using the Richards equation. Due to the high dimensionality of the Richards equation, it is very challenging to solve an optimization-based 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 first propose a structure-preserving adaptive model reduction method using trajectory-based unsupervised machine learning techniques. The adaptively reduced model is then used in the design of an adaptive MHE algorithm. The performance of the proposed algorithms is first compared with a standard MHE based on a small simulated field. Then, the proposed approach is applied to a large-scale real agricultural field and extensive simulations are carried out to show its effectiveness and applicability.