Decision support for dynamic greenhouse climate control strategies
Computers and Electronics in Agriculture
Decision Making Graphical Tool for Multiobjective Optimization Problems
IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
Brief paper: Hamilton-Jacobi-Bellman formalism for optimal climate control of greenhouse crop
Automatica (Journal of IFAC)
Editorial: Modelling and control in agricultural processes
Computers and Electronics in Agriculture
Hi-index | 22.14 |
The problem of determining the trajectories to control greenhouse crop growth has traditionally been solved by using constrained optimization or applying artificial intelligence techniques. The economic profit has been used as the main criterion in most research on optimization to obtain adequate climatic control setpoints for the crop growth. This paper addresses the problem of greenhouse crop growth through a hierarchical control architecture governed by a high-level multiobjective optimization approach, where the solution to this problem is to find reference trajectories for diurnal and nocturnal temperatures (climate-related setpoints) and electrical conductivity (fertirrigation-related setpoints). The objectives are to maximize profit, fruit quality, and water-use efficiency, these being currently fostered by international rules. Illustrative results selected from those obtained in an industrial greenhouse during the last eight years are shown and described.