Intelligent control for a plant production system

For the optimization of long-term plant growth in hydroponics, this paper proposes a hierarchical intelligent control system consisting of an expert system and a hybrid system based on genetic algorithms and neural networks. These two control systems are used appropriately, depending on the plant growth. The plant growth is controlled by the nutrient concentration of the solution. The expert system was used for determining the appropriate setpoints of nutrient concentration through the whole of the growth stages, and the hybrid system for determining the optimal setpoints of nutrient concentration which maximize TLL/SD (TLL: Total leaf length, SD: Stem diameter) only during the initial growth (seedling) stage. In the hybrid system, TLL/SD as affected by nutrient concentration was first identified using neural networks and then the optimal value was determined through simulation of the identified model using genetic algorithms. The setpoints from the expert system were almost similar to those used by a skilled grower. Also, the setpoints from the hybrid system increased the TLL/SD. Thus, this intelligent control technique allowed the optimization of both long-term and short-term plant growth to be realized. This shows that this control technique is suitable for the optimization of such complex and long-term processes as the plant-cultivation process.