Phalaenopsis has been widely popular ornamental plants, due to its unique appearance and excellent market prospects. The commonly used breeding for phalaenopsis is tissue culture at the same time. However, this task involves high repetition and labor intensity. It is still challenging to increase the production of butterfly orchids. This study aims to improve the yield of tissue culture for the labor-saving in phalaenopsis production. A visual servo platform was proposed with the robotic arm gripping. The picking and planting of tissue-cultured seedlings were realized on the conveyor belt. The visual robotic arm was constructed as a 6-degree-of-freedom robotic arm with eyes on the hand, according to the uncalibrated visual servo control. Firstly, the images were captured from the camera. Image detection was implemented to obtain the image coordinates of the roots, stems, and leaves of phalaenopsis tissue-cultured seedlings. Then, the image data was analyzed to determine the angle between the tissue-cultured seedlings and the end clamp of the robotic arm, as well as the gripping point. According to this angle, the end posture of the robotic arm was obtained during gripping. Afterward, the difference in picking point coordinates was calculated between the current and expected image for the tissue-cultured seedlings. The obtained data was then fed into the fuzzy as an input. The acceleration value was derived to control the speed at the end of the robotic arm from the input quantity. The end gripper of the robotic arm was used to track the tissue-cultured seedlings during planting. The acceleration control was utilized to remain relatively stationary in the planting experiment. The gripping of tissue-cultured seedlings was realized to estimate the posture of the gripper after tracking. The tracking speed was also maintained to plant in the given and fixed culture medium. The accuracy of the original YOLOv8 was improved to identify the roots, particularly for the diverse root morphology of Phalaenopsis tissue-cultured seedlings. AKConv and DSConv modules were introduced to extract the root features, resulting in an improved AKDS_YOLOv8. Compared with the original YOLOv8, the recognition accuracy of AKDS_YOLOv8 increased by 8.6 percentage point for seedling roots, the recall value increased by 10.7 percentage point, and the average accuracy increased by 7.4 percentage point, when the intersection-over-union ratio of the real box and the detection box was set to 50%. The recognition performance was improved significantly. A dual-input single-output fuzzy was utilized by the platform. The input was taken as the difference between the actual and expected pixel coordinates of the target, as well as the variation in this difference. The membership function was used to obtain the membership degrees of different fuzzy sets, corresponding to the difference and the variation in difference. The resulting data was then fed into the fuzzy control rule table. The image deblurring was employed to determine the acceleration at the end of the robotic arm in the direction of the conveyor belt. 80 repeated planting experiments were conducted to verify the success rate of planting. A success rate of 82.5% was achieved under the failure situations. The platform can effectively pick and plant phalaenopsis tissue-cultured seedlings on the conveyor belt, indicating the promising application value.