智慧农业导刊2026，Vol.6Issue(14)：13-17,5.DOI:10.20028/j.zhnydk.2026.14.004
基于YOLOv11-FCBG的水稻种子发芽状态检测研究
摘要
Abstract
Rice seed germination rate is an important indicator to evaluate seed quality and vitality,and determines seedling quality and final yield.Traditional manual visual inspection has shortcomings such as low efficiency and inconsistent standards.Aiming at the scene of rice seed germination detection in a petri dish,in order to solve the problem of missed detection and false detection caused by insufficient extraction of fine features and insufficient feature fusion in small target detection,this paper is based on YOLOv11n and introduces FasterNet,C2DA,GLSA,and BiFPN modules to establish a YOLOv11-FCBG model,and uses the Roboflow open source rice seed dataset for training.The results showed that the average accuracy,recall,and mAP50 of the YOLOv11-FCBG model were 0.844,0.88,and 0.921 respectively.Compared with the original YOLOv11 model,the accuracy was increased by 3.05%and mAP50 was increased by 1.77%,which improved the ability to detect rice seed germination.关键词
水稻种子/发芽检测/YOLOv11-FCBG/小目标检测/特征融合Key words
rice seeds/germination detection/YOLOv11-FCBG/small target detection/feature fusion分类
农业科技引用本文复制引用
梁澄河,李佳宝..基于YOLOv11-FCBG的水稻种子发芽状态检测研究[J].智慧农业导刊,2026,6(14):13-17,5.基金项目
2025年度广西高校中青年教师科研基础能力提升项目(桂教科研[2025]1 号,2025KY1409) （桂教科研[2025]1 号,2025KY1409）
广西职业技术学院校级课题(桂职院[2023]121 号,231205) （桂职院[2023]121 号,231205）
