An Efficient and Lightweight YOLOv8s Strawberry Maturity Detection Model  

作　　者：Yiming Cheng Guohao Feng Chunchang Zhang 

机构地区：[1]Merchant Marine College,Shanghai Maritime University,Shanghai 200135,China [2]College of Information and Technology,Shanghai Ocean University,Shanghai 201306,China

出　　处：《Journal of Agricultural Science and Technology(A)》2024年第2期46-66,共21页农业科学与技术（A）

基　　金：funded by the National Engineering Research Center of Special Equipment and Power System for Ship and Marine Engineering and the Shanghai Engineering Research Center of Ship Intelligent Maintenance and Energy Efficiency Control(20DZ2252300).

摘　　要：The manual picking of strawberries is inefficient and costly,limiting scalability and economic benefits.Mechanizing this process reduces labor demands,improves working conditions,and modernizes the strawberry industry.Target detection technology,crucial for mechanized picking,must accurately determine strawberry maturity.This study presents an enhanced YOLOv8s model addressing current machine learning issues like accuracy,parameters,and complexity.The improved model replaces the Bottleneck structure in C2f with the FasterNet network,integrates an efficient multi-scale attention mechanism,and uses the Ghost module in the backbone to reduce computational load while maintaining performance.It also introduces Wise-IoU for bounding box regression loss,improving recognition accuracy.The YOLOv8s-FEGW model achieves a 93.8%mAP in detecting strawberry ripeness,with significant reductions in parameters(36.8%),complexity(34.6%),and model size(37.7%),alongside a 12.7% Frames Per Second(FPS)boost.These enhancements result in excellent detection capabilities,supporting agricultural automation and intelligence.

关 键 词：Automation equipment artificial intelligence efficient and lightweight YOLOv8s 

分 类 号：S66[农业科学—果树学]