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The internet of things has become one of the most important data sources of agricultural big data, therefore automatic quality control of observational data is very important to agricultural production analysis and basic scientific data application. To solve the data missing, outliers excluding, perceived sensing failure and long-term prediction problems of the nonstationary time series data observed in agricultural systems, smooth Gaussian prior of weak assumptions on typical agricultural data was utilized; the dynamic system was built which was characterized by state space equations based on Gaussian process model; through the train set learning, the sensed variation models considered noise distribution were formed, and prediction error bar was provided with uncertainty measurement for the prediction data. For the problems of missing data and outliers excluding of raw data, short-term forecasts based on Gaussian process were adopted to fill with missing data, and its uncertainty measurement was used to detect outliers. Therefore, the outliers were removed and replaced with prediction value, and further sensing failure could be determined on the basis of accumulated outliers in certain time slice. The multi-step iterative method based on the uncertainty spread of input data was given for long-term prediction to track the dynamic trajectory of agricultural sensing data, and an uncertainty measurement could be provided for its predictive value. The data analysis of real sensed collection greenhouse microclimate verifies the feasibility of quality control of agricultural time series data based on Gaussian process in server-side.