摘要
[Objectives]This study was conducted to propose an improved YOLOv8s-based potato identification algorithm,in order to address issues such as low detection precision and high miss detection rate in potato identification tasks.[Methods]The backbone network was replaced with InceptionNeXt,and the CBAM dual attention mechanism was introduced,to enhance the model s multi-scale feature extraction capability.[Results]The improved YOLOv8s algorithm achieved an identification precision of 94.55%,a recall of 85.34%,and an F1-score of 87.37%in potato identification.Compared with the original algorithm,it improved precision by 7.40%,recall by 2.71%,and F1-score by 2.56%.The average processing time per image was reduced by 0.12 s compared with the unimproved algorithm.The results of simulation tests showed a success rate of 98.20%in 2000 simulated identifcation tests.[Conclusions]This study provides a high-precision and robust solution for potato identification tasks.
