Abstract: Identification and counting of specifications are essential to the scallop seedlings before sowing in aquaculture production. Mechanized sowing has become necessary to identify the scallop seedlings with high activity, thereby culturing them in a more sustainable and less harmful environment. In this study, anew automatic device was designed for reasonable specification identification and counting with high efficiency in the scallop seedling. An investigation was made to analyze the system of bottom sowing culture for the seedling of scallop (Patinopectenyessoensis) in Dalian, China. A comparison test was also performed on the island of Changshan, where the scallop seedlings were collected. The specification range of scallop seedling was 20-40 mm in the experiment, where the grading specifications of scallop were set: <25 mm, 25-30 mm, and ≥30 mm. There were two phases for each experiment. In the first phase, an orthogonal experiment was used to analyze the critical influencing factors of specification identification and counting accuracy, and to determine the optimum technological parameters for scallop seedling. In the second phase, the field tests in the actual production situation lasted for 15 d (from October 16th to October 30th, 2019), and 24 groups of comparative tests were conducted. Each group of the test was repeated by 3 times. Specification identification and counting tests were completed by manual labor and machine, respectively. The statistical deviation rate of scallop seedling was calculated at the end of each test, including the total number of workers, and the total time. The optimal technological parameters were achieved for the specification identification and counting device, where the speed of vibration motor was 2 100 r/min, the adjustable exit size of scallop was 50 mm×20 mm, the width limit of the primary queuing guide was 55 mm, the angles of baffle were 45°and 30°, the linear velocity was 0.5 m/s, the vertical distance between sensor and conveyor was 50 mm, the horizontal distance between guide mechanism and conveyor was 25 mm, and the time interval was 25 ms set by the system for the scallop seedling to vertically pass the sensor. In this case, the best accuracy was gained for the specification identification and counting, where the average deviation rate was 3.72%. The influence order of each factor on the average statistical deviation rate of scallop seedling was as follows: the time interval set by the system for the scallop seedling to vertically pass the sensor, the linear velocity, and the vertical distance between sensor and conveyor. The results of comparison tests in actual production showed that the deviation rates were 3.67%-4.65% for the specification identification and counting between mechanical and manual manner, where the average deviation rate was 4.02%. There was no significant difference in the deviation rates of specification identification and counting statistics between mechanical and manual manner (P>0.05). Compared with the screen grading and counting device of scallop seedling before optimization, the statistical deviation rate was reduced by about 0.445%, showing better accuracy and stability in the optimized device. The optimized device of specification identification and counting for the scallop seedling was 5.44 times higher than manual operation, and 0.92 times higher than the screen device of scallop seedling grading and counting. It demonstrates that this device has high efficiency to meet the development needs of the green industry. These findings can support mechanized sowing techniques for the reproductive status of scallop seedling in the aquaculture industry.