Precision Irrigation for Agriculture

Nyhet

Integrating Machine Learning and Optimal Control Strategies

Inbunden, Engelska, 2026

Av Bernard Twum Agyeman, Jinfeng Liu, Canada) Twum Agyeman, Bernard (University of Alberta, Canada) Liu, Jinfeng (University of Alberta

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Produktinformation

Utgivningsdatum2026-03-26
FormatInbunden
SpråkEngelska
Antal sidor256
FörlagJohn Wiley & Sons Inc
ISBN9781394288526

Tillhör följande kategorier

Agronomi och lantbruk inom Naturvetenskap och teknik

ContentsPreface1 Introduction 1.1 Challenges in Irrigation and a Need for Innovation1.2 Current Advances and Unresolved Challenges1.3 Objectives and Organization of the Book2 Background on Soil Moisture Modeling2.1 Introduction2.2 Water Content2.3 Energy Status of Soil Water2.4 Soil Water Retention Curve2.5 Darcy’s Law2.6 Richards Equation2.6.1 Solution of the Richards Equation2.6.2 Sink Term2.7 The General Form of the Richards Equation2.8 Conclusion3 Background on Machine Learning and Model Predictive Control3.1 Machine Learning3.1.1 Supervised Machine Learning3.1.2 Unsupervised Machine Learning3.1.3 Reinforcement Learning3.2 Model Predictive Control3.2.1 Formation of MPC3.2.2 Solving MPC3.2.3 Feasibility of MPC3.2.4 Tuning of MPC3.3 Conclusion4 Soil Moisture and Hydraulic Parameter Estimation in Agro- Hydrological Systems4.1 Introduction4.2 Model Development4.3 Sensitivity Analysis for Parameter Estimability4.3.1 Output Sensitivity Matrix for Spatially Varying Measurements4.4 Parameter Selection through Orthogonal Projection4.5 EKF Design4.6 Simulated Case Study4.6.1 Simulation Results4.7 Real Case Study4.7.1 Study Site and Microwave Sensor Configuration4.7.2 Numerical Representation of the Study Site4.7.3 Sensor Data Preprocessing4.7.4 Analysis of Parameter Estimability and Selection Studies4.7.5 Estimator Design4.7.6 Evaluation Criteria4.7.7 Experimental Results and Discussion4.8 Conclusion5 Adaptive Soil Moisture Estimation Using Performance-TriggeredModel Reduction5.1 Introduction5.2 Model Development and Problem Statement5.3 Model Reduction and Estimation Scheme5.3.1 Adaptive Structure-Preserving Model ReductionStep : Generation of State TrajectoriesStep : Cluster-Based Model Reduction5.3.2 Reduced-Order Adaptive EKF5.3.3 Error Metric and Implementation Algorithm5.4 Field Implementation of the Proposed Framework5.4.1 Results and Discussion5.4.2 Simulation Time Comparison Across Estimation Schemes5.5 Conclusion6 Mixed-Integer Model Predictive Control for Irrigation Scheduling6.1 Introduction6.2 Daily Irrigation Scheduling under Uniform Field Conditions6.2.1 Soil Moisture Model Development6.2.2 Surrogate Model Development6.2.3 Scheduler Formulation6.2.4 Case Study6.3 Irrigation Scheduling in Spatially Heterogeneous Fields6.3.1 Soil Moisture Modeling6.3.2 Scheduler Formulation6.3.3 Case Study6.4 Conclusion7 Multi-Agent MPC for Irrigation Scheduling: A Learning-Based Approach7.1 Introduction7.2 Three-Stage Process for Management Zone (MZ) Delineation7.3 LSTM-Based Modeling of Soil Moisture7.3.1 Richards Equation7.3.2 Training Data Generation7.3.3 Model Design7.4 Mixed-integer MPC with Zone Control for Irrigation Scheduling7.5 Decentralized Hybrid Actor-Critic Agents and the Role of a Limiting MZ7.5.1 Agent Design and Training7.5.2 Multi-Agent MPC Paradigm7.5.3 Triggered Irrigation Scheduling7.6 Application to a Large-Scale Field7.6.1 Delineation of MZs in the Study Area7.6.2 LSTM Network Training for Quadrant7.6.3 Implementation of Hybrid PPO Agents in Quadrant7.6.4 Implementation of Proposed and Triggered Schedulers in Quadrant 7.6.5 Results and Discussion7.6.6 Evaluation of the Proposed Scheduler’s Effectiveness7.7 Conclusion8 Optimizing Irrigation with Semi-Centralized Multi-Agent RL1958.1 Introduction8.2 Semi-Centralized MARL (SCMARL) Framework8.2.1 Addressing Non-Stationarity in SCMARL Frameworks8.2.2 Design of Local Agents8.2.3 Design of the Coordinator Agent8.3 Field-Scale Implementation of the SCMARL Framework8.3.1 Simulation Environment Configuration8.3.2 Agent Setup and Training Process8.3.3 Learning Outcomes of Agents8.4 Evaluation of SCMARL8.4.1 Evaluation of the State Augmentation Strategy8.4.2 Local Agent Policy Alignment with Coordinator8.4.3 SCMARL vs Decentralized MARL: A Comparative Study8.4.4 Results and Discussion8.4.5 Impact of State Augmentation on Agent Performance8.4.6 Local Agents’ Policy Agreement with Coordinator’s Action8.4.7 Assessment of SCMARL Performance and Utility8.5 Conclusion9 Integrating Daily Scheduling and Hourly Control for Precision Irrigation9.1 Introduction9.2 Observer-Based SCMARL Scheduler9.2.1 POMDP Setting9.2.2 EKF for Belief State Estimation9.2.3 Belief State-Based Reward Functions9.3 Advanced Controller Design9.4 Observer-Based SCMARL Implementation9.4.1 Environment Configuration and Training9.4.2 Learning Outcomes of Observer-Based SCMARL Agents9.5 Field Implementation of the Integrated Scheduling and Control Layers9.5.1 Results and Discussion9.6 Conclusion10 Future Directions10.1 Advancing Soil Moisture and Hydraulic Parameter Estimation10.2 Advancing the Design of Multi-Agent MPC-Based Schedulers10.3 Advancing Semi-Centralized Multi-Agent RL Applications10.4 Advancing the Integration of Scheduling and ControlA Relevant Crop and Weather DataA.1 Case Studies of Chapters and A.2 Case Studies of Chapter A.3 Temperature and Evapotranspiration for Case Studies of Chapters7,8,and 9A.4 Crop Coefficient RelationsA.4.1 BarleyA.4.2 Spring Soft WheatB Relevant Soil Hydraulic ParametersB.1 Nominal Parameter Maps Used in Sensitivity AnalysisB.2 Soil Parameters Used in Validation of Chapter C Crop Yield CalculationsD Extended Kalman Filter DesignE Relevant Parameters and Simulation SetupE.1 Richards EquationE.2 LSTM NetworkE.3 Hybrid PPO AgentE.4 Parameters and Weights