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Aiming at the lack of online soil moisture detection system for existing corn sowing equipment and the problems of low detection accuracy and poor environmental adaptation of the existing online soil moisture detection system, a method of soil moisture measurement was presented based on capacitance method and depth compensation, and a set of airborne corn sowing soil moisture online detection system was developed. In terms of electrode plate optimization, the system conducted simulation experiments on capacitor structure optimization parameters with electrode plate spacing, electrode plate thickness, and relative area as experimental factors. The optimal capacitor electrode plate parameters were determined to be as follows: electrode plate spacing of 75.8mm, electrode plate thickness of 0.7mm, electrode plate relative area of 5.073mm2, length of 100mm, width of 50.73mm. The hardware part of the system mainly included FDC2214 capacitive sensor, F4046 pressure sensor, and STM32F103 microcontroller. The capacitive sensor was used to obtain the capacitance value of the soil to be tested, and the pressure sensor was used to obtain the pressure value of the soil to be tested, indirectly inferring the soil depth in the tested area. The system software was developed by using Matlab platform for real-time acquisition, calculation, display, and storage of soil capacitance signals and pressure signals. Based on this system, the influencing factors of soil moisture detection models were explored, and a soil moisture detection model based on BP neural network was constructed. The modeling experiment results showed that when the soil moisture was in the range of 7.23% to 21.14%, the model’s predictive performance indicators R2, RMSE, and RPD were 0.927, 0.008, and 3.70, respectively, with good predictive performance. Finally, the constructed model was integrated into the online soil moisture detection system and bench and field validation experiments were conducted. The results of bench test showed that the fitting coefficient R2 of soil moisture content was 0.852~0.927. The absolute error range of soil moisture prediction results was from -2.89% to 2.57%, and the average absolute error was 1.01%. The field test results showed that the coefficient of determination R2 of the fitting curve between the soil moisture monitoring value and the actual value was 0.842, and the absolute error range of soil moisture monitoring was from -0.96% to 0.45%, with an average absolute error of 0.39%. This indicated that the performance of the detection system developed met the needs of soil moisture monitoring during field operations of corn seeders.