Abstract: In order to improve the total yield of strawberries, reasonable monitoring and control of strawberry diseases is an effective means, a strawberry disease identification algorithm based on improved YOLOv5 is proposed. The detection algorithm uses CSPDarknet as the backbone feature extraction network, which can effectively improve the performance and training efficiency of the model. The EIOU loss function and K-means clustering algorithm are used to improve the convergence speed of the model. At the same time, CBAM attention mechanism is added to the model to improve the detection accuracy, and finally the CBAM-YOLOv5l algorithm based on improved YOLOv5 is constructed. The experimental results show that the improved model improves the detection accuracy and still ensures efficient detection speed compared to the original model. In addition, the trained CBAM-YOLOv5l target detection algorithm achieves an overall average accuracy of 96.52% under the validation set, with an average detection time of 27.52 ms. Compared with YOLOv4, YOLOv4-Tiny, Faster＿R-CNN and other target detection algorithms, CBAM-YOLOv5l algorithm has greater advantages in accuracy. It has good robustness and real-time performance in the actual strawberry orchard environment, and it can meet the needs of strawberry disease identification accuracy and reliably prompt the health status of strawberries, so as to timely achieve precise pesticide application and other protection measures.