Intelligent Control for Plant Production System

Abstract 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-tern and short-term plant growth to be realized. This shows that this control technique is suitable for the optimization of such complex and long-terns processes as the plant-cultivation process.

[1]  Tetsuo Morimoto,et al.  Identification of Hydroponics in an Advanced Control System of the Greenhouse , 1992 .

[2]  Kenneth de Jong,et al.  Adaptive System Design: A Genetic Approach , 1980, IEEE Trans. Syst. Man Cybern..

[3]  Tetsuo Morimoto,et al.  Optimal control of physiological processes of plants in a green plant factory , 1995 .

[4]  Yasushi Hashimoto,et al.  Computer Integrated Plant Factory Based on Artificial Intelligence , 1992 .

[5]  Yasushi Hashimoto,et al.  Growth optimization of plant by means of the hybrid system of genetic algorithm and neural network , 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).

[6]  Stephen A. Billings,et al.  Non-linear system identification using neural networks , 1990 .

[7]  T. Miyata,et al.  Retroviral protease-like sequence in the yeast transposon Ty 1 , 1985, Nature.

[8]  G. M. Pasqual,et al.  Development of an expert system for the identification and control of weeds in wheat, triticale, barley and oat crops☆ , 1994 .

[9]  Avron Barr,et al.  The Handbook of Artificial Intelligence , 1982 .

[10]  C.M.M. van Winden,et al.  SOILLESS CULTURE TECHNIQUE AND ITS RELATION TO THE GREENHOUSE CLIMATE , 1988 .

[11]  Marie-Josée Cros,et al.  Chapter 9 – USING EMPIRICAL KNOWLEDGE FOR THE DETERMINATION OF CLIMATIC SETPOINTS: AN ARTIFICIAL INTELLIGENCE APPROACH , 1993 .

[12]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[13]  H.-J. Tantau,et al.  Optimal control for plant production in greenhouses , 1991 .

[14]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[15]  Toru Kato,et al.  Studies on the Characteristics of Seedlings Raised in Pot under Various Conditions and Their Productivity in Eggplant and Sweet Pepper (5) Effects of Temperature and Fertilizer Application Level during Period of Raising Seedling on the Eggplant , 1987 .

[16]  Panos J. Antsaklis,et al.  Neural networks for control systems , 1990, IEEE Trans. Neural Networks.

[17]  Peter J. Gawthrop,et al.  Neural networks for control systems - A survey , 1992, Autom..

[18]  J. Rennie,et al.  DNA's new twists. , 1993, Scientific American.

[19]  Tetsuo Morimoto,et al.  System Identification of Plant Response for Optimal Cultivation in Greenhouses , 1984 .

[20]  Yasushi Hashimoto.  Computer integrated plant growth factory for agriculture and horticulture , 1991 .

[21]  D. Ehret,et al.  Effects of Osmotic Potential in Nutrient Solution on Diurnal Growth of Tomato Fruit , 1986 .

[22]  R. W. McClendon,et al.  Methods for Optimal Control of the Greenhouse Environment , 1992 .

[23]  R. Sequeira,et al.  Automating the parameterization of mathematical models using genetic algorithms , 1994 .