Improved YOLOv5s based identification of pests and diseases in citrus
Accurately identifying pests and diseases in citrus can be used to timely reduce the econom-ic 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 intro-ducing 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 YO-LOv5s-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 indicat-ed that this method can be used for detecting the diseases and pests of citrus in complex natural environ-ments.
deep learningpests and diseasesYOLOv5starget detection
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