Accurately identifying the severity of strawberry leaf disease is essential for precise disease control. However, methods based on image classification had a rough division of disease severity and fuzzy classification boundary, while methods based on semantic segmentation had high computational costs and long inference time. To address these problems, a real-time strawberry disease diagnosis method was proposed based on interactive bilateral feature fusion network (IBFFNet). The IBFFNet was a lightweight model containing a context path and a spatial path to extract semantic and detail features from the input image, respectively. Furthermore, an attention spatial pyramid pooling module was constructed to extract multiscale semantic features from the context path, and an edge enhancement module was designed to enrich edge detail information in the spatial path. Finally, the multiscale semantic feature and detail information were aggregated for precise leaf and lesion area segmentation. The percentage of lesions in the leaf area was the estimated severity. The method achieved a promising trade-off between accuracy and speed on the strawberry leaf disease diagnosis dataset. On the strawberry leaf disease diagnosis dataset, the mIoU of IBFFNet2_Seg was 77.8% with 40.6f/s on a single NVIDIA GTX1050. In the test set, an R2 value (coefficient of determination) of 0.98 was achieved, which denoted that the IBFFNet2_Seg could accurately predict the severity of the three diseases. This study paved the way for the precise control of strawberry disease.