Research on Fruit Fly Identification in Berry Orchards Based on Improved YOLOv8n Algorithm

Wang Wei; Yang Jiansheng; Zhang Mei; Chen Zhe; Zhang Qunying; Liu Nietianhe

doi:10.14083/j.issn.1001-4942.2025.02.023

Abstract：To improve the identification efficiency of fruit flies in berry orchards to effectively guide fruit fly control,this study proposed a lightweight detection algorithm through structure modification to the YOLOv8n framework.Firstly,a new C2fGhostV2 module was constructed using the GhostNetV2 bottleneck to replace the residual blocks of all C2f modules in the backbone of YOLOv8n,in order to reduce the computa-tional cost and improve the detection performance.Secondly,the BiFPN was reconstructed by adding convolu-tional layers and increasing jump connections,and a more efficient L-BiFPN structure was designed to replace the FPN+PAN structure in the neck of YOLOv8n,in order to improve the feature fusion efficiency and expres-sion capability.Thirdly,the MBConv was used to replace the residual blocks of all C2f modules in the neck of YOLOv8n,and a new C2fMBC module was constructed,in order to improve the computational efficiency and enhance the ability to reuse the features.The experimental results showed that the parameter amount,weight,and number of floating-point operations(FLOPs)of the improved YOLOv8n were reduced by 48.50％,43.98％and 32.10％compared with the original YOLOv8n algorithm,and its precision,recall and mean aver-age precision(mAP)were 97.40％,96.60％and 98.32％,respectively,which also outperformed the original algorithm.Overall,the improved algorithm put forward in this study significantly reduced the algorithm com-plexity but enhanced the recognition accuracy,and featured lightweight and easy deployment,so it could meet the requirement of fruit fly recognition task on mobile devices in the berry orchards,thus providing a reference for fruit farmers to precisely control fruit flies.