More-accurate control, resulting in increased yield, higher quality, and minimizing costs to the grower remain the main driving forces of greenhouse climate research.Studies using techniques such as computational fluid dynamics, plant sensor measurements, and tracer gas analysis of greenhouses are now widespread. Feedback processes such as mass and energy transfers between the crop and its environment are the focus of this paper. Of the panoply of possible strategies for modeling greenhouse climate that could have been used, this study investigates greenhouse climate modeling in terms of biological cybernetics. The approach here is simply to represent the most important components of the greenhouse climate with a view to developing an accurate neural controller, the production of which involves system identification to produce first a neural reference model. For the neural model to have flexibility in predicting the dynamics of abrupt weather changes and the complex feedback processes between the crop and its environment requires good training data. In this paper, different greenhouse control strategies are reviewed, and a biological cybernetic model is constructed. The model is then used to train a neural network, with the training results presented.
Cybernetic Approach to ModelingGreenhouse Dynamics system identification approach. Alternatively, quasi-static models consider the greenhouse as being comprised of two distinct parts, namely a crop model and a greenhouse model (Schmidt, 1996). The two sub-models vary in a turn-based way during simulation with the speed at which each part can react to such stimulus as adverse changes in weather. In essence, these models are assuming that the exchange processes for temperature, water vapor and C0 2 between the crop and the greenhouse environment are minimal, and that these processes are slow. Plant processes, for example photosynthesis exhibit almost instantaneous response to stimulus and as such can cause serious errors with these models. Similarly, problems occur when solar radiation levels fluctuate due to scattered clouds (Tap et al., 1993).Ultimately, such significant errors affect the seasonal performance of the crop. A more effective strategy is to treat the crop as a biological process to be controlled by monitoring the crop's reaction to climate. This is what is termed the 'speaking plant concept' (Udink ten Cate et al., 1978), and this has been successfully implemented to good effect in controlling humidity (de Jong & Stranghellini, 1996, Schmidt, 1996.This approach ideally entails the use of sensors to monitor the crop's progress.However, progress can be made toward this ideal by modeling the feedback exchanges between the crop and its environment.In this study, a biological cybernetic framework of the model is provided by a set of coupled linear differential equations. The framework introduces numerous variables that enable many other processes to be included, such as plant dynamics and meteorological data. The development of the model and the use of a set...