Mechanistic Crop Growth Model Predictive Control for Precision Irrigation in Rice

Rapid urbanization and climate change exacerbate water scarcity. Thus, irrigation conservation is an important endeavor in food security . Conservation efforts have centered on the ability to monitor and manage the amount of water in rice fields. In this framework, there have been advances in simple rule-based irrigation scheme such as safe alternate wetting and drying (Safe-AWD). Safe-AWD provides a robust rule set that mitigates yield reduction due to the decrease in irrigation. However, the extent of the adverse effects from induced drought stress are often hard to predict across different factor combinations of crop variety, environment, and management practices. In this light, the integration of crop models offers additional precision in the amount of irrigation for specific conditions. Moreover, the availability of crop models allows the use of methods in control theory for irrigation management. In particular, Model Predictive Control (MPC) is robust and able to handle multi-objective and practical real world constraints. Importantly, it has been studied for irrigation set point tracking in water balance based models (WB-MPC). Thus to address variable crop response, this work augments MPC irrigation with a crop model to track growth trajectories of biomass, leaf area index, and grain formation throughout the planting season (CG-MPC). Presented are simulations that compare irrigation management techniques - namely traditional ponding, SafeAWD, WB-MPC, and CG-MPC. Based on water savings and yield reduction, results show that Safe-AWD has the greatest irrigation conservation while WB-MPC and CG-MPC can be designed to have comparable water usage. However, CG-MPC produced the best yield reduction and has minimal variance across different field scenario simulations. This presents design opportunities for tuning risk trade-offs between yield reduction and water savings.