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September 16, 2025Frontiers in Plant ScienceOpen Access

Research on detection and counting method of wheat ears in the field based on YOLOv11-EDS

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Authors

JLJing LiSouth China Agricultural UniversityZWZhenchang WangHohai UniversityXLXiaoqing LuoJiangnan University

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Implication

This research demonstrates enhanced detection precision for wheat ears, indicating improvements in smart agriculture methods.

Key Points

  • The YOLOv11-EDS model improved precision by 2.0 percentage points and recall by 3.5 percentage points, achieving better yield estimation.
  • Experimental results featured a significant increase in mAP@0.5 values, with gains of 1.5 percentage points over existing models.
  • Incorporation of the Dysample operator and other optimizations lead to reduced computation, lowering model parameters to 2.5 M and operations to 5.8 G.
  • This study highlights a practical advancement for smart agriculture, enabling accurate monitoring of crops in complex environments.

Cite This Study

Li et al. (2025) studied this question.

synapsesocial.com/papers/68d4508231b076d99fa5826chttps://doi.org/10.3389/fpls.2025.1672425
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