黑龙江八一农垦大学学报2026，Vol.38Issue(3)：104-112,9.DOI:10.3969/j.issn.1002-2090.2026.03.013
基于三种组合改进YOLOv8模型的马铃薯芽眼识别方法
Potato Bud Eye Recognition Method Based on Three Combinations to Improve the YOLOv8 Model
摘要
Abstract
To achieve rapid and accurate identification of potato seed tuber eyes,a potato eye recognition method based on three combined improved YOLOv8 models was proposed.Firstly,the Bi-directional Feature Pyramid Network(BiFPN)was replaced,and the P2-level small target detection layer was integrated to enhance the model's ability to detect multi-scale eye targets and improve the model's robustness.Secondly,the Convolutional Block Attention Module(CBAM)was introduced to improve the model's detection performance for small target eyes.Finally,the Non-Maximum Suppression(NMS)algorithm was optimized to increase the recall rate.The results showed that the model achieved a precision(P)of 97.34％,a recall(R)of 94.23％,a harmonic mean F1-Score of 95.76％,a mean Average Precision(mAP@0.5:0.95)of 95.47％,and the recognition time for a single image was 16.3 ms on average.This model performed excellently in the potato eye recognition task with stronger anti-interference capability.It could provide an important theoretical reference for eye recognition in the intelligent cutting process of potato seed tubers and hold significant practical value for promoting the full-process mechanization development of the potato industry.关键词
YOLOv8算法/芽眼识别/马铃薯/种薯切块Key words
YOLOv8 algorithm/bud eyes detection/potato/seed potato cutting分类
信息技术与安全科学引用本文复制引用
吴海风,黄操军,白宇,赵红梅..基于三种组合改进YOLOv8模型的马铃薯芽眼识别方法[J].黑龙江八一农垦大学学报,2026,38(3):104-112,9.基金项目
黑龙江省哲学社会科学研究规划项目(23JYC066) （23JYC066）
大庆市哲学社科科学规划项目(DSGB2025070). （DSGB2025070）
