In order to accurately detect edible roses and their maturity in the field and realize the automatic picking of flowering roses,an improved model based on YOLOv5s was proposed to solve the problem of poor recognition accuracy caused by factors such as light and occlusion in the field.The growth state of edible roses at bud,picking and abortive flow-ering stages was detected.Firstly,in order to enhance the ability of multi-scale feature fusion,the feature fusion structure was improved.Secondly,multi-branch structure training was used to improve the accuracy,and the neck network C3 mod-ule was improved.Finally,in order to improve the ability of feature information extraction,a fusion attention module was added to the neck network to make the model focus on the detection target and reduce the false detection and missed detec-tion of roses.The mean average precision of the improved model was 3.6 percentage points higher than that of the original model,reaching 90.4％,and the detection accuracy of roses in three flowering periods was improved.The results of this study provide a more accurate method for detecting edible roses at different flowering stages in unstructured environment.
维普
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