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In order to further improve the accuracy and speed of grape disease identification, the YOLO v8 model was improved. Firstly, the GhostNetV2 backbone feature extraction network was introduced to improve the feature extraction ability and recognition performance of the model. Secondly, the SPPFCSPC pyramid pooling was embedded to improve the speed while keeping the receptive field unchanged. Thirdly, the GAM-Attention mechanism was added to reduce the information reduction and amplify the feature information to speed up the recognition. Finally, Focal-EIoU was used as the loss function to improve the bounding box regression performance of the detection model, and finally the grape leaf disease identification model YOLO v8-GSGF was formed. The recognition test verified that the YOLO v8-GSGF model can achieve 97.1% recognition accuracy and 45.3ms inference time, and can achieve high-precision identification of various grape diseases. The results of the ablation test showed that all the improvements had an effect on the recognition performance of the model, and the GhostNetV2 backbone network had the most obvious effect on the model. The YOLO v8-GSGF model can achieve 98.2% recognition accuracy and 43.7ms inference time in the ablation test, which was 8.6 percentage point and 20.4ms higher than that of the original YOLO v8 model. Compared with the current mainstream recognition model, the YOLO v8-GSGF model had better performance, better recognition accuracy and speed, and the curve chart also intuitively showed that the performance of the YOLO v8-GSGF model was superior, and the improvement effect was remarkable, which can meet the needs of grape orchard disease identification and had the potential for practical application.