Design of Differential GPS System Based on BP Neural Network Error Correction for Precision Agriculture

Abstract

Precision agriculture is the new tendency of agricultural development all around the world today. During the implementation process, precision agriculture is been required to collect information on crop diseases, pests and fertilizers at any time. The acquisition of this information depends on the precise position information. Considering the problem that the positioning accuracy of the traditional GPS system is low or the other signal needs to be sent to correct the position, a GPS position based on neural network is designed to correct the precision problem. The GPS module was used to receive GPS signal, and the position information was extracted by the MCU. Then, the real-time location information is displayed on the screen. The back propagation neural network was used to generate a prediction model of error value between the measured data and real data. This model can predict and compensate the errors of measured values. Finally, the measured data and the corrected data are shown on the screen. The precision of GPS positioning designed in this paper is 10 times higher than that of traditional GPS, meeting requirement with high precision of the information-based agriculture.