Abstract: This study aims to ensure the high-quality operation requirements of vegetable seedlings in a plant factory. A series of tests were performed on the precision seeding device of the vegetable pot tray with high accuracy. A device was designed and optimized for online miss-seeding detection and intelligent reseeding, according to the pneumatic roller-type device of precision seeding. Furthermore, a programmable logic controller (PLC) was used as the control core, in order to ensure the stability of the control system in the production line. The real-time miss-seeding detection of suction holes was realized to predict the miss-seeding location of the pots. The intelligent fixed-point was timely completed for the precise reseeding of the missed-seeding location pots. The performance of miss-seeding detection was then optimized in the production process, according to the structural characteristics of the pneumatic roller-type seed-metering device. A specific arrangement of photoelectric sensors was used to realize the real-time detection of miss-seeding suction holes. Subsequently, a dynamic reseeding matrix was constructed corresponding to the suction holes and pots of the pot tray, according to the pot's position and number of the pot tray. The miss-seeding location of the pot tray was also predicted. Furthermore, the intelligent reseeding device was designed and optimized to realize intelligent and accurate reseeding at the miss-seeding holes. The miss-seeding location of the pots was extracted from the data from the dynamic reseeding matrix. Taking vegetable seeds named Zhongshuang No.11 as the test materials, the suction holes miss-seeding detection and pots miss-seeding location prediction were carried out on the reliability of miss-seeding detection. The results that the average accuracies of miss-seeding suction holes detection and pots miss-seeding location prediction were 98.82% and 100%, respectively. Furthermore, the Box-Behnken method was used to evaluate the operational performance of the intelligent reseeding device. The relationship between the performance indexes (single seed qualified index, multiple seeding index, and miss-seeding index) and the influencing factors (negative pressure of suction needle, diameter of suction needle, and position pressure of vibrator) was constructed using multi-objective optimization. The optimal working parameters of the intelligent reseeding device were determined to be the negative suction pressure of 10.19 kPa, the diameter of 0.67 mm, and the vibration pressure of 0.07 MPa after a large number of tests and analyses. In this case, the values of working parameters were rounded to facilitate the test. It was found that the average single-seed qualified index was 94.80%, the multiple seeding index was 2.94%, and the miss-seeding index was 2.26%. The intelligent reseeding device under this condition fully met the reseeding requirements of the miss-seeding detection and reseeding device. The performance test of the miss-seeding detection and reseeding device was carried out to verify the model. Once the productivity of the miss-seeding detection and reseeding device was 100 plates/h, the single-seed qualified index of the miss-seeding detection and reseeding device increased to 98.18%, compared with 93.96% before reseeding. When the productivity of the miss-seeding detection and reseeding device was 300 plates/h, the single-seed qualified index of the miss-seeding detection and reseeding device increased to 97.89%, compared with 93.18% before reseeding. The test results fully met the requirements of high precision seeding in vegetable pot seeding devices in plant factory and field conditions. The practical application value was offered to improve the seeding performance of vegetable pot seeding devices. The finding can provide technical support for the production of high-quality vegetable pot seedlings.