Knowledge-Based Optimal Irrigation Scheduling of Agro-Hydrological Systems

Agricultural irrigation consumes about 70% of freshwater globally every year. To improve the water-use efficiency in agricultural irrigation is critical as we move toward water sustainability. An irrigation scheduler determines how much water to irrigate and when to irrigate for an agricultural field. To get a high-resolution irrigation-scheduling solution for a large-scale agricultural field is still an open research problem. In this work, we propose a knowledge-based optimal irrigation-scheduling approach for large-scale agricultural fields that are equipped with center pivot irrigation systems. The proposed scheduler is designed in the framework of model predictive control. The objective of the proposed scheduler is to maximize crop yield while minimizing irrigation water consumption and the associated electricity usage. First, we introduce a structure-preserving model reduction technique to significantly reduce the dimensionality of agro-hydrological systems. Then, based on the reduced model, an optimization-based scheduler is designed. In the design of the scheduler, knowledge from farmers is taken into account to further reduce the computational complexity of the scheduler. The proposed approach explicitly considers both the irrigation time and the irrigation amount as decision variables to keep the crop within the stress-free zone considering the weather uncertainty and heterogeneous soil types for large agricultural fields. The proposed approach is applied to three different scenarios with different soil types, crops, and weather uncertainty. The results show that in all the conditions, the scheduler is capable of keeping the crops stress-free, which results in maximum yield and, at the same time, minimizes water consumption and irrigation events.