Important to NASA’s Advanced Life Support program is the development of an autonomous, dynamic, self-contained bioregenerative life support system for future, long duration spacecraft and space stations to provide fresh food, air, water and to recycle waste products. These systems will rely on plants to rejuvenate the air and produce food through the process of photosynthesis and purify water through the process of transpiration. An intelligent, autonomous, reliable, and robust control system must be developed and applied to dynamically manage, control and optimize plant-based life support functions to allow the efficient growth of plants, providing the maximum amount of life essentials while using minimal resources. System identification and modeling of plant growth behavior must first be developed to characterize plant growth functions in order to develop an efficient control system.
We have developed an artificial neural network model to characterize the photosynthesis process of soybean crops under various environmental conditions. It is a 2-layer feedforward neural network architecture which inputs the crop type, age, and the environmental conditions of the crop canopy: carbon dioxide level, light intensity, temperature, and relative humidity and outputs the predicted net photosynthesis or assimilation rate produced under these conditions. The neural network model was trained from controlled environment soybean crop experiments conducted at Rutgers University in New Brunswick, New Jersey where dynamic plant responses over a range of environmental conditions were collected. This paper will discuss in more detail the motivation for developing the crop model, the neural network model and performance and the crop experiments and data collected by Rutgers University.