The precision detection of wheatplanter seeding quantity is the key of the automation control and the foundation of realizing precise seeding. Currently, existing seeding detection methods mainly include photoelectricbased methods, imagebased methods and capacitancebased methods. For the photoelectricbased method, the detection accuracy of photoelectric sensor is affected by the vibration, light, temperature and other factors on the farmland. For the imagebased method, its high precision detection provides a new way to improve the performance of wheatplanter seeding. However, image processing technology requires special equipment with high cost, and cameras are easy to be interfered by external light. Consequently, it is difficult to be widely applied in the complex environment on the farmland. Compared with photoelectricbased and imagebased methods, the capacitancebased method is less affected by light and dust and thus has a strong environmental adaptability. Aimed at the problem of precision detection of wheatplanter seeding quantity, a precise detecting system for wheatplanter seeding quantity was developed based on the capacitance sensor. The corresponding parallel plates were designed to improve the detecting accuracy of the capacitance sensor. Then, the relationship between capacitance value and the quantity of wheat seeds under two modes of singleseed falling and severalseed falling was investigated in detail. For the mode of singleseed falling, a differential dynamic threshold method was proposed to detect the number of wheat seeds. The experiment results indicated that the maximum relative error was 1.54%. For the mode of severalseed falling, the least squares regression model between wheat seeds quantity and capacitance integral value was established at interval of 5r/min ranged from 20r/min to 55r/min. The experiment results showed that according to the principle of the actual speed and the closest speed to select the corresponding regression model，the system had high detection accuracy for different seeding speeds, and the relative error was between -2.16% and 2.23%. Consequently, the proposed system can achieve high detection accuracy under the different seeding modes and different seeding speeds.