Abstract: Grassland degradation has posed a serious threat to ecological stability, forage yield, and the sustainable animal husbandry in recent years. Agricultural robots (such as unmanned aerial vehicle (UAV) platforms) can be expected to monitor and reseed the degraded grassland patches for ecological restoration. However, existing UAV operations have been limited to the low accuracy of patch identification and patch positioning, due to easy target loss or unstable tracking. In this study, a computer vision was proposed to integrate the advanced object detection and tracking algorithms for the efficient detection and positioning of grassland patches. The YOLO series model was used to detect the patch. An adaptive feature pyramid network was introduced to enhance multi-scale feature fusion, thereby improving detection accuracy in complex grassland backgrounds. Lightweight convolution was used for channel compression and nonlinear activation, while the expression of deep features was optimized for the computational efficiency. The Swin Transformer sliding window self-attention mechanism and residual convolution structure were adopted to establish long-range dependencies between patches and the grassland background within the local receptive field. Input RGB patch images were converted to the YUV color space. The single-channel luminance map was extracted as the input to enhance the luminance contrast of the grassland and patch texture structures. In terms of temporal consistency and target tracking, the DeepOCSORT algorithm was combined to perform multi-target tracking of patches in consecutive image sequences. The results showed that the improved SCTD-YOLO detector significantly outperformed the baseline YOLOv8. Ablation experiments indicated that the better performance was achieved to combine the different modules. The accuracy and recall rate increased to 94.7%, and 86.2%, respectively, using the improved SCTD-YOLO model, indicating the best performance; The average precision and mAP increased by 2.4 percentage points and 2.2 percentage points, respectively. The high robustness was also obtained to significantly reduce the missed detection and false rates under different lighting conditions, complex backgrounds of grassland texture, partial patch occlusion by plants, and significant changes in patch size. In multi-target tracking, the algorithm with the DeepOCSORT tracker was effectively maintained the consistency of patches in multi-frame sequences, with an IDF1 of 85.15%, an MOTA of 88.2%, and an MOTP of 86.71%, significantly reducing false detections and ID switches. DeepOCSORT model was still stably maintained the target trajectories in the scenarios of rapid camera panning, grass leaf shaking caused by wind, and dynamic background, significantly improving the reliability of long-term monitoring. Finally, the latitude and longitude coordinates were predicted after image modeling and external parameter calculation, compared with high-precision RTK-GPS measurement coordinates. The spatial positioning error was calculated using the Vincenty formula. The improved SCTD-YOLO and stable tracking algorithm were combined to significantly reduce the overall positioning error, with an average error of 0.316 1 m and an error range of 0.278 m to 0.423 m. The higher positioning accuracy and error stability were found in all test samples, effectively improving the overall consistency of target detection, tracking, and positioning. The overall positioning accuracy was fully met the requirement of less than 0.5m in intelligent reseeding of degraded grassland patches. The SCTD-YOLO and DeepOCSORT framework were integrated for patch detection, tracking, and positioning in complex multi-scenario and multi-interference environments for agricultural robots. The high efficiency and reliability of grassland patch reseeding can be expected for the decision-making generalization of agricultural robot vision in different agronomic models and environmental conditions, sustainable grassland, resource conservation, and ecological restoration. Further exploration can be conducted on seasonal monitoring and adaptive reseeding using multi-source remote sensing data.