Abstract: In order to achieve low-cost, convenient, and efficient identification and detection of strawberry diseases, and improve the efficiency of strawberry planting and production, based on YOLOv5 model, an efficient channel attention（ECA） mechanism was introduced to study and construct a strawberry disease recognition model. The embedded and software engineering technology was applied to develop and implement a strawberry disease recognition terminal device. The system consisted of modules such as image acquisition, image detection, display of detection results, and data transmission. The system realized real-time collection of strawberry images and disease identification and checking functions. The system was tested using Kaggle’s strawberry disease detection dataset, and the experimental results showed that the system could effectively identify diseases such as strawberry powdery mildew fruit, and strawberry corner spot, and leaf spot. And compared with YOLOv5, there is a significant improvement in AP0.5∶0.95、AP0.5、AP0.75、APM、APL. The system has the advantages of efficiency, convenience, and real-time online, which can be widely applied in the field of strawberry production, thereby effectively improving the efficiency of strawberry disease identification and detection.