of four grape crop images from the Plant Village dataset, achieving an accuracy of 99.34%. [PERSON] et al. [7] fused the features extracted from ResNet50 and ResNet10 to build a grape disease identification model, eventually achieving 99.08% accuracy in identifying four grape diseases in the Plant Village dataset. [PERSON] [8] used transfer learning in combination with the MobileNetV3 network to achieve a final accuracy of 99.4% for six grape diseases. [PERSON] et al. [9] achieved 96.05% accuracy in identifying six grape pests and diseases in complex environments based on the ShuffleNet V2 network combined with the MDF decision-making method. [PERSON] et al. [10] combined ghost convolution with a transformer to propose a GeT grape pest identification model, which eventually achieved an accuracy of 98.14% for 11 grape pests and diseases. [PERSON] et al. [11] extracted features using a fully connected layer, removed irrelevant features from the feature extraction vector using the proposed variance technique and used a classification algorithm to classify the resulting features, achieving an accuracy of 98.7% for the detection of six grape diseases. [PERSON] et al. [12] first used the GSLL algorithm to reduce image noise and then used CASM-AMFMNet to achieve an accuracy of 95.95% in identifying five grape diseases. [PERSON] et al. [13] created a dataset of grape leaves affected by nutrient deficiencies from farmland in a controlled environment, before customizing a CNN model and validating it using an n-fold cross-validation method, ultimately achieving an accuracy of 97.07% for the identification of four grape diseases. [PERSON] et al. [14] achieved 86.29% accuracy in recognizing seven grape diseases in AI Challenger 2018 through implementing feature fusion of different depths through the RFFB module and introducing CBAM to extract valid disease information. [PERSON] et al. [15] combined long-range radio technology with deep learning to transmit images over long distances, achieving an accuracy of 98.33% in identifying four grape diseases in the Plant Village dataset, even with a packet loss rate of 50%. Although much research has been performed on the classification of grape diseases, image classification can only determine the disease type of the whole picture. It cannot, however, directly locate and mark the location of the disease; thus, it has some limitations in real-time detection. Fewer studies have previously been conducted on the target detection of grape diseases. [PERSON] et al. [16] based their Faster R-CNN detection algorithm and introduced the Inception-v1, Inception-ResNet-v2, and SE modules to achieve a recognition rate of 81.1% for the four grape diseases. [PERSON] et al. [17] first enhanced the images of black rot in grapes in the Plant Village dataset via bilinear interpolation, before introducing the SPP module to the backbone network of YOLOv3 and replacing the loss function from IOU to GIOU to achieve an accuracy of 95.79% for the detection of black rot in grapes. [PERSON] et al. [18] achieved an accuracy of 99.93% in identifying four grape diseases in the Plant Village dataset based on Faster RCNN, introducing multi-task learning and a dual attention mechanism construct. However, most of these images are from the Plant Village dataset, which contains only four grape images, i.e., healthy leaves, black rot, brown spot, and Esca, which are mainly used to demonstrate object detection tasks in laboratory environments with relatively simple backgrounds that differ from those of grape diseases in natural environments; therefore, they may affect the generalization ability of the model. In addition, the detection speed of these algorithms is slow, which may have some drawbacks for application scenarios with high real-time detection requirements.