The increasing improvement and maturity of 5G network technology provides a new developable direction for real-time processing of agricultural production data. For the monitoring of sensor data in the agricultural production process, this paper proposes a data collection network (GCN) system consisting of a perception layer, a network layer, and an application layer as a method for collecting research data. After obtaining the research data, a GCN with a forest-like network structure is used to construct the GCN components for data exchange between the data collection system and the gateway as well as the optimization of large-scale data transmission between networks. The ARMA time series data model with high accuracy and short training time was selected to perform short-term time series data prediction in agricultural production scenarios by describing the dynamic characteristics of time series data. AMRA modeling is performed on the experimental data, and the calibration accuracy of the model is 88.81% on average for 10 runs, and it can be as high as 89.16% and as low as 88.27%.
