Abstract: Precision seeders have been widely used in large-scale sowing in recent years, particularly with the ever-increasing mechanization in modern agriculture. Among them, the pneumatic dibbler of precision seeder was normally utilized in Xinjiang of western China, in order to realize the function of one hole and one seed for the seed saving and the high yield of cotton during sowing. However, the working state cannot be directly evaluated in the traditional sowing operation, because the seeding process was confined in a closed state of the pneumatic dibbler. Such sowing failures as missed- and multiple-seeding cannot be completely avoided for the high sowing quality. One way can be selected to reseed at the position of missed sowing after the emergence of seedlings. As such, the cotton cannot open to be harvested in time, leading to the low quality and yield of cotton. In terms of multiple-seeding, another way can be the high-cost thinning of seedlings in the late stage. Therefore, it is very necessary to evaluate the sowing state along with the detection of seeding. However, the traditional detection device cannot be suitable for the pneumatic dibbler under the conditions of temperature and humidity. In this research, a real-time monitoring system was designed and developed for the seeding of pneumatic dibbler using an interdigital capacitive sensor. Firstly, the sensor was arranged, according to the structure and working parameters of the pneumatic dibbler. The output capacitance of the sensor was collected by the micro-capacitance acquisition system (Pcap02 chip). A series of verification and bench tests were then conducted to evaluate the system performance and simulation, including the interdigital capacitive sensor, the micro capacitance acquisition system, and the experimental prototype dibbler. The performance test showed that the measurement errors of the system capacitance and the prediction model of cotton seed quality were within 1% and less than 3%, respectively. It inferred that the micro-capacitance acquisition system fully met the requirements of measurement. The simulation verification test showed the accurate system in the missed-seeding test. The misjudgment rate was less than 3% in the normal sowing test. Specifically, the normal sowing was misjudged as the miss- and multiple-seeding in the test, due to the difference in the quality of cotton seed. The misjudgment rate was less than 4% in the multi-seeding test. The multiple-seeding was misjudged as the uni-grain sowing, because the quality of the cotton seed combination was approximated to the multiple-seeding judgment threshold. The bench test showed that the overall monitoring accuracy of the system remained at 93%, only a relatively decrease from the certain vibration caused by the motor. Among them, the overall monitoring accuracy of normal sowing, missed-seeding, and multi-seeding were 96.4%, 94.04%, and 93.9%, respectively, at the working speed of 30-45 r/min, which were lower than the overall monitoring accuracy of the simulation verification test. The variance of the test data was analyzed through the F test, in order to judge the difference between the system and the machine vision. The F values were measured for the number of normal-, missed- and multiple-seeding using the system and machine vision, which were lesser than F0.05 (6.39). The P values were greater than 0.05 for the number of normal-, missed- and multiple-seeding. Therefore, there was no significant difference between the system monitoring and the measured using machine vision, indicating the excellent detection accuracy and stability of the system. The finding can provide great significance for the precision sowing of cotton.