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The greenhouse is a multivariate, nonlinear, time-varying and time-lag system. The establishment of an accurate greenhouse temperature prediction model can effectively ensure the effectiveness of intelligent temperature control. In order to solve the problem of difficulty in modeling the temperature mechanism of greenhouses, this paper proposes a method based on improved Support Vector Regression to establish greenhouse temperature prediction, The PSO algorithm is used to optimize the hyper-parameters of the SVR. Using real greenhouse data to verify the effectiveness of the proposed model. Experimental results show that compared with the traditional BP neural network prediction model, the temperature prediction model in greenhouse based on improved SVR has significantly improved prediction accuracy and better prediction capabilities. Therefore, the greenhouse temperature prediction model based on improved SVR proposed in this paper can well characterize the actual greenhouse system.

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