Abstract: Cucurbit grafting and seedling cultivation can often require the directional seeding in modern agriculture. But manual directional seeding of the cucurbit rootstocks grafting can suffer from the high labor intensity, the low operational efficiency and directional accuracy. It is an extremely urgent need for the highly efficient and precise directional seeding equipment in the seedling cultivation market. The conventional tray seeders are also limited to the directional seeds. In this study, a robotic system was designed for the precision directional seeding of the white pumpkin seeds using machine vision. A vibratory seed supply mechanism was integrated with an adsorption effector, a handling mechanism, and a machine vision system. The directional seeding robot was accurately identified the morphological features of the cucurbit rootstock seeds. Some information, such as the seed bud angle, was acquired using machine vision. The seed pickup and orientation actuator were then guided to pick up, reorient, and place the seeds. The synchronous directional sowing of five seeds per row was realized after following procedures. Firstly, an image processing with the OpenCV was developed to extract the seed contour, and then identify the critical features, including the bud position, geometric center, and bud point angle. Secondly, a suction end-effector was designed for the negative pressure adsorption and servo-driven rotation. A dynamic model was constructed for the internal flow field within the suction nozzle. The key parameters were then determined, such as the nozzle shape, diameter, and adsorption pressure. An electromagnetic vibratory feeding with a conveyor belt was implemented to singularize and evenly distribute the seeds. Finally, these subsystems were integrated into a functional prototype. An orthogonal rotation regression experiment was performed with the transport speed, suction negative pressure, and suction height as the experimental factors. The performance was evaluated as the qualified seeding rate, miss-seeding rate, seeding accuracy, and operational efficiency. The experiment revealed that the primary influencing factors on the qualified seeding rate were ranked in the descending order of the suction negative pressure, suction height, and transport speed. The optimal combination of the parameters was identified as a transport speed of 1000 mm/s, a suction negative pressure of 60 kPa, and a suction height of -1 mm. The better performance was achieved in a high qualified seeding rate of 96.67%, a low miss-seeding rate of 3.33%, and an operational efficiency of 3123 seeds per hour under these optimal conditions. Most importantly, the seeding accuracy was 92.24%. Machine vision was integrated with the high precision of the mechanical orientation. A directional seeding robot was developed for the cucurbit rootstocks. The "bud recognition, posture adjustment, and precise delivery" were combined into a unified system. Directional seeding accuracy and operational efficiency were significantly enhanced to develop the oriented seeding robots in the advanced horticulture and grafting applications. At the same time, the directional seeding equipment can enhance the mechanical grafting efficiency and seeding accuracy.