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In response to problems such as different graphics, densely distributed, and difficult to identify in the field environment, the study of the number of rapeseed seedlings based on the YC-YOLO v7 algorithm was carried out. Introduce the depth-separated convolutional module in the ELAN of the original model YOLO v7 to improve the extraction ability of the model on small features. By adding the CBAM attention mechanism module to the feature layer output by the main network, the model of the models identification of small targets is enhanced. Replace the loss function CIOU to WIOU, which improves the quality of the anchor frame. In order to expand the model of the model for the goal, the SPPF space pyramid structure was constructed. The test results show that the average accuracy of the improved YC-YOLO v7 model was 94.0%, the accuracy was 89.8%, the recall rate was 91.2%, the reasoning speed increased by 16.1.f/s, and the floating-point computing volume was reduced by 2.56×1010. Compared with the other phase model YOLO v5s, SSD, and second-stage model Faster R-CNN, the average accuracy increased by 12.8 percentage points, 17.8 percentage points, and 20.3 percentage points, respectively. The improved YC-YOLO v7 model was deployed to the PC, and an oilseed rape seedling detection and identification system was constructed using the PYQT5 framework, with the average accuracy of the system detection being greater than 90%, which can provide technical support for the accurate counting of oilseed rape seedlings in the field environment, and provide effective support for the farmers to judge the quality of the breeding and the effect of sowing.