Accurately identifying pests and diseases in citrus can be used to timely reduce the economic losses.A common method for detecting pests and diseases in citrus based on the improved YOLOv5s model was proposed to solve the problems that the existing models of detection cannot accurately identify multiple types of pests and diseases of citrus in the natural environment.The model was improved by introducing the ConvNeXtV2 model and constructing a CXV2 module to replace the C3 module of YOLOv5s， enhancing the diversity of extracted feature.The dynamic detection head DYHEAD was added to improve the processing ability of the model for different spatial scales and task targets.The CARAFE upsampling module was used to improve the efficiency of extracting feature.The results showed that the improved YOLOv5s-CDC had a mean recall rate and average precision of 81.6% and 87.3%，4.9 percentage points and 3.4 percentage points higher than that of the original model，respectively.Compared with the detection with other YOLO serial models in multiple scenarios，it had higher accuracy and stronger robustness.It is indicated that this method can be used for detecting the diseases and pests of citrus in complex natural environments.