Abstract: Regenerative ability is closely related to the number of regenerated buds in the ratooning season, even the yield of ratooning rice. The traditional detection cannot fully meet the large-scale production in the rice regenerated buds, due to contact damage, subjective inefficiency, and low repeatability. In this study, a multi-target tracking of regenerated buds was proposed for high-precision counting using Micro-CT (computed tomography) and an improved DeepSORT. Micro-CT imaging was first adopted to capture the cross-section video stream of rice stem in the ratooning season. Then, the YOLOv5s network was used as the tracking detector of regenerated buds, while the improved DeepSORT tracking was studied to achieve the accurate tracking and counting of rice regenerated buds. The ID discrepancy was then optimized to improve DeepSORT tracking. The matching accuracy of the multi-target tracking increased using the feature of continuity between each frame of CT tomogram images, indicating the substantially improved ID switch. Finally, the height of the regenerated buds was calculated to discriminate the effectively regenerated buds using the location information of the tracking object. The experimental results showed that the Mean Average Precision (mAP) values of YOLOv5s were 97.3% and 99.1% for the regenerated buds and stalks, respectively, in the target detection. The multi-object tracking accuracy (F_MOTA ) , higher order tracking accuracy(F_HOTA) , and ID switch of the improved DeepSORT were 77.61%, 61.73%, and 6, respectively, in the multi-target tracking, compared with the original. Furthermore, the F_MOTA and F_HOTA were improved by 1.51% and 8.5%, respectively, whereas the ID switch was improved by 94%. The multi-object tracking efficiency of the DeepSORT and the improved were 25, and 24 frames per second, respectively, without a significant decrease in the efficiency. The system and manual measurements of 104 pots of ratooning rice were used to verify the regenerated buds, where the correlation coefficient square R² of 0.983, the root mean square error of 3.460, and the average absolute percentage error of 5.647%, indicating a better consistency with the manual measurement. The ratio of the regenerated buds to the number of the stem was computed for the early ratooning ability of rice. The correlation analysis was also performed between the ratooning ability of two rice varieties in 38 pots and the actual yield in the ratooning season. It was found the R² values were 0.795 and 0.764, respectively, indicating a significant positive correlation between the regenerative ability and rice yield. In conclusion, the novel nondestructive way was achieved to detect the regenerated buds in the early regenerative ability measurement. The finding can also provide important technical support for the ratooning rice breeding.