Abstract
Maintaining environmental conditions for proper plant growth in greenhouses requires managing a variety of factors; ventilation is particularly important because inside temperatures can rise rapidly in warm climates. The structure of the window installed in a greenhouse is very diverse, and it is difficult to identify the characteristics that affect the temperature inside the greenhouse when multiple windows are driven, respectively. In this study, a new ventilation control logic using an output feedback neural-network (OFNN) prediction and optimization method was developed, and this approach was tested in multi-window greenhouses used for strawberry production. The developed prediction model used 15 inputs and achieved a highly accurate performance (R2 of 0.94). In addition, the method using an algorithm based on an OFNN was proposed for optimizing considered six window-opening behavior. Three case studies confirmed the optimization performance of OFNN in the nonlinear model and verified the performance through simulations. Finally, a control system based on this logic was used in a field experiment for six days by comparing two greenhouses driven by conventional control logic and the developed control logic; a comparison of the results showed RMSEs of 3.01 °C and 2.45 °C, respectively. It confirmed the improved control performance in comparison to a conventional ventilation control system.
Highlights
Greenhouses are a widely used agricultural system that can provide optimal growing conditions for crops, regardless of season, as the controlled inside environment is less affected by exterior weather conditions
The prediction results for the ~11,000 validation samples in the learning process yielded a 0.99 an offset of 1.53, and a total RMSE of 0.78 ◦ C
This study proposed an optimal ventilation control system for managing greenhouse temperature composed of a neural-network-based prediction model and optimization node, verified our approach through simulations and field experiments
Summary
Greenhouses are a widely used agricultural system that can provide optimal growing conditions for crops, regardless of season, as the controlled inside environment is less affected by exterior weather conditions. Greenhouse crop growth is influenced by CO2 level, photosynthetically active radiation, and temperature; the latter two directly affect photosynthesis under diurnal conditions. The most important method for maintaining greenhouse temperature is natural ventilation, which mixes external and internal air conditions but is very difficult to artificially control. Greenhouses are highly nonlinear and strongly coupled systems that are strongly influenced by weather and the behavior of actuators used for climate control [5]. Many studies have proposed advanced control methods for greenhouse environments [6,7,8]. Modern greenhouses use multiple-paned windows for more active natural ventilation, but it is more difficult to automatically
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