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Found 8 papers, 0 papers with code
Performance triggered adaptive model reduction for soil moisture estimation in precision irrigation
Soil moisture estimation based on limited soil moisture sensors is crucial for obtaining comprehensive soil moisture information when dealing with large-scale agricultural fields.
Optimizing the switching operation in monoclonal antibody production: Economic MPC and reinforcement learning
Most of the processes for industrial mAb production rely on batch operations, which result in significant downtime.
Integrating machine learning paradigms and mixed-integer model predictive control for irrigation scheduling
The results demonstrate the superiority of the proposed scheduler compared to a traditional irrigation scheduling method in terms of water use efficiency and crop yield improvement for both growing seasons.
Maximizing soil moisture estimation accuracy through simultaneous hydraulic parameter estimation using microwave remote sensing: Methodology and application
Improving the accuracy of soil moisture estimation is desirable from the perspectives of irrigation management and water conservation.
Control invariant set enhanced safe reinforcement learning: improved sampling efficiency, guaranteed stability and robustness
This work proposes a novel approach to RL training, called control invariant set (CIS) enhanced RL, which leverages the advantages of utilizing the explicit form of CIS to improve stability guarantees and sampling efficiency.
Knowledge-based optimal irrigation scheduling of agro-hydrological systems
The typical agricultural irrigation scheduler provides information on how much to irrigate and when to irrigate.
LSTM-based model predictive control with discrete inputs for irrigation scheduling
The development of well-devised irrigation scheduling methods is desirable from the perspectives of plant quality and water conservation.
Soil moisture map construction using microwave remote sensors and sequential data assimilation
Secondly, measurements obtained from the microwave sensors are assimilated into the field model using the extended Kalman filter to form an information fusion system, which will provide frequent soil moisture estimates and predictions in the form of moisture content maps.
