A novel lightweight algorithm for detecting diseases in crop leaf based on YOLOv8n，EMD-YOLO was proposed to accurately and rapidly identify diseases，reduce costs of manual diagnosis，and minimize the impact of leaf diseases on the production and quality of crops.The algorithm integrated the multi-scale dilated attention （MSDA），EffectiveSE attention mechanism，DySample upsampling，and Wise-IoU loss function.MSDA was combined with multi-scale spatial convolution and attention mechanisms to increase the efficiency of extracting multi-scale feature，while EffectiveSE strengthened the selection of feature and improved the representation performance of model.DySample upsampling preserved important features to enhance the map resolution of feature and the detection performance.Wise-IoU loss function optimized intersection over union （IoU） computation to improve the localization accuracy of model.The results showed that the accuracy，mAP@0.5，and weight of EMD-YOLO was 96.3%，92.8%，and 4.85 MB，respectively.The accuracy and mAP@0.5 of YOLOv8n increased by 3.0 and 3.6 percentage points compared with that of the baseline model YOLOv8n，respectively，while the weight decreased by 1.4 MB.It is indicated that EMD-YOLO has good generalization and is suitable for detecting diseases in crop leaf with mobile devices.