YOLOv11-FS model-based detection of pollen viability in citrus

LIU Liyuan; ZHANG Xuelin; CHEN Hong; LI Weifu; LIAO Jianhua; XIE Kaidong; WU Xiaomeng; GUO Wenwu; CHEN Yaohui

doi:10.13300/j.cnki.hnlkxb.2026.03.007

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YOLOv11-FS model-based detection of pollen viability in citrus

Agricultural artificial intelligence | 更新时间：2026-11-05

- YOLOv11-FS model-based detection of pollen viability in citrus
- Journal of Huazhong Agricultural University Vol. 45, Issue 3, Pages: 77-86(2026)
- 作者机构：
  
  1.华中农业大学信息学院，武汉 430070
  2.果蔬园艺作物种质创新与利用全国重点实验室，武汉 430070
  3.华中农业大学工学院，武汉 430070
- 作者简介：
- 基金信息：
- DOI：10.13300/j.cnki.hnlkxb.2026.03.007
  CLC： TP391.4;S666
- Received：02 July 2025，
  
  Published：25 May 2026
- 稿件说明：
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刘力源，张学林，陈洪，等.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.

LIU Liyuan,ZHANG Xuelin,CHEN Hong,et al.YOLOv11-FS model-based detection of pollen viability in citrus[J].Journal of Huazhong Agricultural University,2026,45(03):77-86.
刘力源，张学林，陈洪，等.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. DOI： 10.13300/j.cnki.hnlkxb.2026.03.007.

LIU Liyuan,ZHANG Xuelin,CHEN Hong,et al.YOLOv11-FS model-based detection of pollen viability in citrus[J].Journal of Huazhong Agricultural University,2026,45(03):77-86. DOI： 10.13300/j.cnki.hnlkxb.2026.03.007.

摘要

为促进无核柑橘品种培育、满足高品质柑橘生产需求，提出一种新的花粉活力检测模型YOLOv11-FS。通过人工采集和专家标注的方式，构建了适用于花粉活力检测的数据集，改进了YOLOv11深度神经网络；检测过程中，采用Focal-EIOU损失函数替换YOLOv11的EIOU损失，改善对不均衡样本的检测性能，结合Soft-NMS提升检测框精度，克服柑橘花粉颗粒抱团明显、体积较小且背景复杂等挑战。结果显示，改进的YOLOv11-FS模型在花粉检测任务方面表现优秀，预测的花粉活力与真实的花粉活力误差仅为0.70百分点，可育花粉和不可育花粉检测的召回率、精确率和

-score分别达98.76%、99.67%、99.22%和94.87%、98.89%、96.84%，满足花粉活力检测基本需求。以上结果表明，该检测方法可实现柑橘花粉活力的高效、精准识别。

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 vitality was proposed by improving the YOLOv11 deep neural network. A dataset suitable for detecting pollen vitality 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 impr

ove the precision of detection boxes， overcoming challenges including obvious 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

⁃score of detecting fertile pollen and sterile pollen reached 98.76%， 99.67%， 99.22%， and 94.87%， 98.89%， 96.84%， respectively， 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.

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references

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