Optimal Heat Treatment to Delay The Ripening Proces of Tomato using Genetic Algorithms

Abstract

An optimal pattern of the heat treatment for tomato was investigated using an intelligent control technique consisting of neural networks and genetic algorithms. An objective function was given by the reciprocal number of the color development from green to red for evaluating the ripening of tomato. The control process was divided into l-step. Firstly, the color development was identified using neural networks. Then, l-step set-points of temperature which maximize the objective function were sought through a simulation of the identified model using genetic algorithms. The genetic algorithms allowed an optimal heat treatment to be successfully determined by simulation of an identified neural network model. Finally this optimal heat treatment was applied to an actual system. The result showed that the optimal heat treatment indicated better result in maintaining the color development of fruit than the conventional one.