Abstract: The seeding amount of the seeding tray can determine the quality and productivity in the process of rice factory seedling raising. It is a high demand for the precise control of the seeding amount in the seedling tray, particularly for the simple operation, low labor intensity, and high efficiency. In this study, an intelligent regulation device was proposed for the seeding amount of seeding trays in the rice seedling raising production line. Among them, the STM32F429 microprocessor was used as the control core. The weighing data of the seedling tray was also collected and then processed in the system. The working principle was established for the intelligent regulation and control device of the seeding amount in the seeding tray. The key components were also designed and optimized for the composition of the device. A mass weighing mechanism was developed with the height difference between the support and drive wheels, in order to combine the characteristics of closely spaced seedling trays. The experimental observation of the travel of the seedling tray was carried out to determine the key parameters (such as the installation height difference between the support and drive wheels, and the static distance of the seedling tray). Double diffuse reflectance photoelectric sensors were used to detect the side eave of a continuously moving seedling tray within a set detection distance, according to the height difference between the side eave and the material inside the seedling tray. The control system was used to collect the duration of the diffuse reflectance photoelectric sensor. A duration threshold was set to enable the accurate forecasting of the seedling tray's arrival at the weighing position. The model was then established for the variation in the seeding amount of seedling trays of rice sprouts, in relation to the speed (frequency) of the seeding wheel motor. As such, the seeding amount of seedling trays was regulated for the different varieties of rice sprouts using statistical analysis and information feedback techniques. Therefore, three species of hybrid rice sprouts were selected for the variation pattern of rice sprouts. At the same time, the regular model was further integrated for the same rice varieties, in order to determine the regulation equation of the seeding amount of seeding trays. The correction factor was also set for the different rice varieties. The intelligent regulating device was used to detect and regulate the seeding amount of seeding trays at a production rate of 500 trays/h. Specifically, the average detection accuracy of the seeding amount in the seeding tray was 94.82%, whereas, the average coefficients of variation were 2.5%, and 4.04%, respectively, for the hybrid and conventional rice after systemic regulation. The test results show that better regulation and high detection accuracy were achieved in the intelligent regulation device, which fully met the requirements for the seeding amount control in the rice factory seedling raising production line. The finding can provide practical application to improve the intelligent level of rice seedling raising production line for the high quality of seedling raising.