华中农业大学学报2026，Vol.45Issue(3)：77-86,10.DOI:10.13300/j.cnki.hnlkxb.2026.03.007
基于YOLOv11-FS模型的柑橘花粉活力检测
YOLOv11-FS model-based detection of pollen viability in citrus
摘要
Abstract
A dataset of detecting pollen viability in citrus was constructed to promote the breeding of citrus seedless varieties and meet the demand for high-quality production of citrus.A new model of detecting pollen vi-tality was proposed by improving the YOLOv11 deep neural network.A dataset suitable for detecting pollen vi-tality was established through manual collection and expert annotation.The YOLOv11 deep neural network was improved to develop the YOLOv11-FS model.The Focal-EIOU loss function was used to replace the EIOU loss of YOLOv11 to enhance detection performance on imbalanced samples during the process of detection.Soft-NMS was incorporated to improve the precision of detection boxes,overcoming challenges including obvi-ous clustering,small size,and complex backgrounds of pollen grains in citrus.Results showed that the improved YOLOv11-FS model performed excellently in the task of detecting pollen,the predicted pollen viability deviates from the true pollen viability by only 0.70 percentage points.The recall,precision,and F1-score of detecting fer-tile pollen and sterile pollen reached 98.76%,99.67%,99.22%,and 94.87%,98.89%,96.84%,respective-ly,meeting the basic needs of detecting pollen vitality.It is indicated that the method of detection establshed can achieve efficient and accurate identification of pollen vitality in citrus.关键词
柑橘/花粉活力检测/YOLOv11-FS/Soft-NMS/Focal-EIOU损失Key words
citrus/detection of pollen viability/YOLOv11-FS/Soft-NMS/Focal EIOU loss分类
信息技术与安全科学引用本文复制引用
刘力源,张学林,陈洪,李伟夫,廖健华,解凯东,伍小萌,郭文武,陈耀晖..基于YOLOv11-FS模型的柑橘花粉活力检测[J].华中农业大学学报,2026,45(3):77-86,10.基金项目
国家重点研发计划项目(2023YFD1200103) （2023YFD1200103）
教育部学科突破先导项目(JYB2025XDXM701) （JYB2025XDXM701）
中央高校基本业务费专项(2662024SZ002) （2662024SZ002）
