Abstract: Abstract: Economy-based optimal control of greenhouses is an important technique to reduce the operating cost and increase the crop yield. In General, the structure of a typical greenhouse control system consists of two layers: the objective optimization layer and the process control layer. The aim of the former is to obtain the target trajectories of environmental states; while in the latter, environmental states are tuned to track those obtained trajectories. Based on this framework, some relevant models to the greenhouse system, such as greenhouse microclimate model, crop growth and yield model, energy consumption predicting model and CO2 consumption predicting model, need to be built, and some constraints over environmental states and control inputs must be determined. However, most greenhouse climate models and crop growth models proposed in literature are research-oriented rather than based on practical cultivation. The biggest difference between the two is that research-oriented models solely focus on the optimal set-points of internal climatic parameters for maximum crop yield, ignoring the abilities of control actuators, the ambient climate change, and the overall energy consumption. From a practical point of view, the latters are certainly necessary in greenhouse models. It would be difficult to achieve realistic results if the models are incomplete. The lack of reliable greenhouse models has become the greatest difficulty for greenhouse optimization and control. In this paper, the latest trends in greenhouse climate models, crops growth models, energy consumption models, and CO2 consumption models are reviewed in details. The main shortcomings of current models can be summarized as follows: (1) although some models obtained by ample mechanisms, their structures are excessively complicated, such that the corresponding computations are very expensive, which makes it difficult to design an efficient controller based on them. This class of models interprets the real physical laws with large number of parameters and complex structures, and Vanthoor's model and TOMGRO are examples; (2) some models are too simple to accurately reflect the relationships between greenhouse environment and crop growth. Generally, only the dynamics of air temperature and humidity in greenhouses are described in this class of greenhouse climate model, such as Albright's model, while the dynamic of CO2 concentration, which is an important environmental factor to affect photosynthesis, is not included. Furthermore, the influence to crop growth of environmental factors is always partially reflected in simplified crop growth models, e.g. in radiation and thermal effectiveness model, only air temperature and radiation is used to describe the accumulation and allocation of dry matter; (3) only single control input variable is included in many greenhouse climate models, such as the thermal environmental model of solar greenhouse, this is because most greenhouses are not equipped with relevant control actuators. Generally, these greenhouse climate models are used to guide the structure design, material selection, or management of greenhouse production. Additionally, although some greenhouse climate models include various control inputs, including heating, fogging, ventilation and CO2 injection, the dynamic response of control actuators are still not described adequately, and so, they can't be used for greenhouse climate control. In order to obtain a suite of efficient greenhouse models, great efforts need to be made to solve the following key problems: (1) the unknown mechanisms of some processes need to be explored further for a mathematical expression of their input-output relationship. Provided with these results, some complex models can be improved; (2) based on a complex model with detailed mechanistic expressions, some dynamic sub-processes can be reconstructed or simplified to reduce the model complexity; (3) new approaches and theories about system modeling and model validation can be developed.