most citedIntegrating machine learning paradigms and mixed-integer model predictive control for irrigation scheduling

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eess.SY2023★ 10 cited

Integrating machine learning paradigms and mixed-integer model predictive control for irrigation scheduling

Bernard T. Agyeman, Mohamed Naouri, Willemijn Appels +2

The agricultural sector currently faces significant challenges in water resource conservation and crop yield optimization, primarily due to concerns over freshwater scarcity. Tradi…

eess.SY2023

Sensitivity-Informed Parameter Selection for Improved Soil Moisture Estimation from Remote Sensing Data

Bernard T. Agyeman, Erfan Orouskhani, Mohamed Naouri +4

Improving the accuracy of soil moisture estimation is required for advancing irrigation scheduling and water conservation efforts. Central to this task are soil hydraulic parameter…