Multi-objective evolutionary optimization in greenhouse control for improved crop yield and energy tradeoffs

The worldwide increase in demand for fresh fruits and vegetables has led to a search for strategies to manage greenhouses in ways that not only meet this demand, but that are also economically viable and environmentally sustainable. A well-established approach for managing greenhouse microclimate is through the automatic control of its mechanical systems such as heaters, ventilators, and shade screens. Such a system is a form of closed-loop control, but only with respect to the greenhouse microclimate, rather than the crop being grown. In practice, conventional greenhouse control is criticized for this focus on climate control instead of crop production, as well as the complexity of managing these systems due to an excessive number of user settings [1]. A more comprehensive form of closed-loop optimal control in greenhouses has been proposed to provide a better degree of control by adjusting the greenhouse climate in response to the growth of the crop being cultivated, but it is still dependent on the external climate around the greenhouse and can lack acceptable alternatives due to the nonlinear nature of the interactions between environmental conditions and plant growth. Unfortunately, monitoring of the real-time response of the crop is not viable for this type of closed-loop control - what can be used instead is a rather sophisticated state model of crop production so that the microclimate conditions can be controlled in order to optimize their effects on the predicted seasonal crop production. Further, this model and the greenhouse microclimate model into which it is integrated must be executable in a short enough timeframe to allow running it thousands of times to optimize the performance of the controller for a given greenhouse structure and location. Having developed such a model, we propose using a form of evolutionary multi-objective optimization to discover a suite of user-selectable control strategies that balance crop productivity with the financial costs of greenhouse climate control. Each of the Pareto-optimal controllers discovered by this approach defines a range of conditions to be maintained via specified control actions, depending upon the crop state and external environmental conditions. Due to the large number of candidates present as the output, the decision-making process will be aided by considering common user preferences as well as algorithmically examining the robustness of solutions in the final Pareto-optimal frontier.