Abstract: 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 40.6 f/s on a single NVIDIA GTX1050. In the test set, an R~2 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.