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Artificial Neural Network Controller for Climate Control inside the Greenhouse Model

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  Computers in Agriculture and Natural Resources, 4th World Congress Conference, Proceedings of the 24-26 July 2006 (Orlando, Florida USA) Publication Date 24 July 2006  701P0606.(doi:10.13031/2013.21890)
Authors:   Tapankumar Basu, Vijaya Ravindra Thool, Dr. Ravindra C. Thool, Harsh R. Birhade
Keywords:   Greenhouse, Optimization, Neural Network, Backpropogation, Controllers

Greenhouse is the most practical method of achieving the objectives of protected agriculture where the natural environment is modified by using sound engineering principles to achieve optimum plant growth and yield. The methodology proposed in this paper applies artificial intelligence (AI) techniques to the modeling and control of some climate variables within a greenhouse. The nonlinear physical phenomena governing the dynamics of temperature and humidity in such systems are, in fact, difficult to model and control using traditional techniques. The paper proposes a framework for the development of soft computing-based controllers in modern greenhouses. Among intelligent system like a fuzzy logic, genetic algorithm and neural network controller we are using an Artificial Neural Network Controller. Optimization climate parameter like temperature, humidity, soil moisture, and sunlight inside the greenhouse is to be controlled by actuating a cooler, foggers, drippers and lights respectively according to the necessary condition of crops. For measurement purpose various sensor output decisions is taken by the computer which is working on Neural Network backpropogation algorithm. Thus different control action takes place and climate inside the greenhouse is controlled as per the need of the crop.

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