In sapphire processing by the Kyropoulos method, the detection of seeding timing is the core technology. Existing detection methods are incapable of tracking spoke feature motion on the liquid surface and cannot effectively guide sapphire seeding. Therefore, this paper proposes an innovative visual-perturbation-velocity-fitting method for sapphire seeding timing detection. First, the concept of perturbation velocity, which can reflect the flow state of the melt surface, is presented. Then, the position of spoke feature points is obtained by computer vision and calculated by multi-frame interactive periodic detection. The fitting function model corresponding to the perturbation velocity in different flow states is established. Finally, the seeding timing is determined by the transition of the flow state and the stability of the flow. The experimental results show that the proposed method can improve the seeding success rate from 76.7% to 93.3% and the average successful seeding time from 5.6 h to 4.5 h. © 2022 Elsevier Ltd