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Abstract Water management is an important aspect in modern agriculture. Irrigation systems are becoming more and more complex, trying to minimize the water consumption while ensuring the necessities of the plants. A fundamental requirement to define efficient irrigation policies is to be able to estimate the water status of the plants and of the soil. In this context, precision agriculture addresses this problem by using the latest technological advancements. In particular, most of the works in the literature aim to develop highly accurate estimations under the assumption of the availability of a dense network of sensors. Although this assumption may be adequate for intensive farming (e.g. greenhouses), it becomes quite unrealistic in the context of large-scale scenarios. In this work, we propose a novel observer-based architecture for the water management of large-scale (hazelnut) orchards which relies on a network of sparsely deployed soil moisture sensors along with a weather station and on remote sensing measurements carried out by drones with a pre-defined periodicity. The contribution is twofold: i) First a novel model of the water dynamics in an hazelnut orchard is proposed, which includes the water dynamics in the soil and in the plants, and ii) then, on the basis of this model and of the available measurements, the use of a Kalman filter with intermittent observations is proposed, taking also into account the availability of the weather station measurements. The effectiveness of the proposed solution is validated through simulation.

A novel Observer-based Architecture for Water Management in Large-Scale (Hazelnut) Orchards