Detection method of grape disease in orchard environment based on Swin-TDL algorithm

In order to accurately detect grape diseases under complex environmental factors in orchards, a grape disease detection model Swin-TDL based on Swin Transformer is proposed. Kmeans++ clustering algorithm is used to calculate the anchor frames of the model input images to improve the detection accuracy. The Swin Transformer network is used as the backbone network of Swin-TDL for more accurate acquisition of target feature information. Feature pyramid networks and path aggregation networks are used to fuse information from feature layers of different depths in the backbone network to enhance the semantic and localization information of detection targets. The SIoU loss function is used as a boundary regression prediction loss function for improving the speed of training and the accuracy of model inference. Soft-NMS is used to post-process the target bounding boxes to improve the detection rate of occluded and overlapped targets. Finally, model training and performance testing were carried out in the field grape disease dataset. The experimental results showed that the average accuracy of the Swin-TDL model was 92. 7% and the average detection time was 15. 3 ms. The comprehensive performance of Swin-TDL was better than the comparative detection algorithm, which could provide a reference for the research of grape plant protection equipment.