Plant growth models have been developed for the purpose of increasing our knowledge of plants, improving agricultural practices, as a tool in landscaping, and for the purpose of optimization and control. Modelling efforts have taken several different approaches depending on the intended use of the model. Many plant growth models are empirical, and apply fitted functions without considering the biological mechanisms underlying plant growth. These models have the benefit of simplicity, but are not mechanistic and therefore cannot be applied to a variety of species or over a wide range of conditions [1]. In contrast, complex metabolic models give a more complete description of reactions taking place within the cells, and are useful tools for studying plant development [2][3]. However, because of the large complexity, these models are typically over-parameterized and unidentifiable, making them unsuitable for prediction and control purposes. Process based models and functional-structural models attempt to bridge this gap. These models consider at least some plant processes and interactions between the plant and the environment. Process based models typically refer to those models that do not take plant morphology into account [4][5], while functional structural models generally include an empirical view of plant architecture [6][7][8][9] . These models work well under certain environmental conditions, but they are usually developed for plant growth under field conditions, and therefore neglect the effect of some important environmental variables (for example carbon dioxide and oxygen concentration). More mechanistic models, based on the reaction kinetics of the most important processes, should be applicable over a wider range of condtions.