Abstract: The effect of heat treatment on the respiration rate and optimal control for respiration of tomato fruit was studied. The storage temperature was the control input varying from 5～40℃ and the changes of tomato respiration rate was monitored as an output. The purpose was to minimize the respiration by the temperature (e.g. heat treatment). An objective function was given by the reciprocal number of the average value of the respiration rate. The dynamic changes in the respiration rate affected by temperature, was first identified using neural networks. A neural-network model identified here showed a good agreement with the observed responses of respiration. The temperature control process was divided into 4 steps (l=4) and the optimal 4 step points of temperature which maximize the objective function were obtained using genetic algorithms. The optimal control method was found effective in reducing respiration rate of tomato fruit during the storage.