Abstract: Smart agriculture is a technical solution to achieve precision in agriculture. The smart agriculture system can monitor various environmental parameters of plant growth in real time, and many predictive models are applied to simulate the changing trend of crop growth environment and provide a basis for scientific decision-making. In recent years, many scholars have proposed prediction model algorithms for time series, which have achieved good results in terms of prediction stability. In order to further improve the prediction accuracy of time series, a combined prediction model based on autoregressive integrated moving average model and wavelet neural network is proposed. The combined model combines the advantages of two single models, the autoregressive integrated moving average model is used for fitting the linear part of the sequence, and the wavelet neural network is used for correcting the residual error to make the fitting curve closer to the actual value. History within the greenhouse temperature data is used to validate the precision of combination model. Finally, the results of the combined model and the traditional prediction model are compared. The results show that the combined model has higher accuracy and better fitting effect for greenhouse temperature prediction, and the calculation efficiency is about 20% higher than that of the traditional model prediction algorithm.

Key words: smart agriculture, time series, ARIMA model, WNN, combined forecasting model