Strawberry identification method based on improved YOLOv8n
To realize the identification and detection of strawberry in the facility ridge in order to avoid the low recognition rate caused by fruit stacking,leaf and branch shading,and uneven light in the strawberry growing environment,and change the status quo of strawberry picking relying on manual labor.A strawberry recognition model based on improved YOLOv8n was proposed.The MBCA module was constructed as the backbone network feature extraction module of YOLOv8n.The AVCStem module replaced the three C2f modules of the neck network,and the GSConv replaced the common convolution of the neck network,keeping the lightweight and further improving the accuracy.The experimental results showed that the R,P and mAP of the improved YOLOv8n model were 96.8％,93.8％,92.4％,respectively.This study could realize the identification of ripe strawberries,which was helpful to further promote the development and application of intelligent strawberry picking robots.
万方数据
维普
