Agricultural diseases can cause early defoliation of crops and weakened photosynthesis,which can affect crop quality and reduce farmers′ incomes.Aiming at the problem of target miss detection exception caused by small targets, complex background and unstable natural light during the initial occurrence of the diseases,this paper proposes a YOLOv4 detection algorithm that integrating lightweight networks.Firstly,the trunk network is simplified and multi-scale group convolution is enhanced to improve the anti-interference ability of the mode in the complex backgrounds.Secondly,the lightweight space channel expand (SCE) attention mechanism is designed to reduce the impact of detail information loss in the deep network.Finally,the pyramid structure with the feature of skip connection is applied for the replacement of integration module with path aggregation network (PAnet) feature to further realize the model lightweight.Experimental results show that the improved algorithm reaches 84.17% of mAP50 and the detection speed is 50 FPS in the dataset of this paper,which is 0.71% and 10 FPS higher than that of YOLOv4 detection algorithm,that meets the requirements of the detection accuracy and speed of agricultural diseases on the mobile devices.