Modeling and control for closed environment plant production systems.

Area of Science:

• Agricultural Engineering
• Plant Science
• Control Systems

Background:

• Controlled environment agriculture (CEA) is crucial for sustainable food production.
• Optimizing crop yields requires precise management of environmental variables.
• Existing crop models often lack integrated control strategies for dynamic environments.

Purpose of the Study:

• To develop and evaluate a computer program for integrated crop production and control in CEA systems.
• To simulate crop growth and development under various environmental conditions.
• To implement a model-based predictive controller for managing environmental perturbations.

Main Methods:

• Developed time-series crop models for wheat, soybean, and potato using multivariate polynomial regression (MPR).
• Integrated crop models with a model-based predictive controller.
• Used simulated data from DSSAT crop models for MPR fitting.
• Controller adjusts light, temperature, and CO2 set points based on a cost function.

Main Results:

• The program successfully simulates crop growth and development under nominal and off-nominal conditions.
• The model-based predictive controller effectively compensates for environmental disturbances.
• Nonlinear polynomial equations accurately represent crop responses.
• Control signals optimize production scheduling by minimizing errors and control effort.

Conclusions:

• The developed computer program provides a robust framework for optimizing crop production in CEA.
• Integrated crop modeling and predictive control enhance system resilience to environmental fluctuations.
• This approach holds significant potential for improving efficiency and yield in controlled agricultural systems.