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相关概念视频

Extraction: Advanced Methods00:56

Extraction: Advanced Methods

1.1K
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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相关实验视频

Updated: Jan 16, 2026

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
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使用改进的YOLOv8n-seg算法精确地进行类细分和茎采摘点定位.

Han Li1,2, Zirui Yin1,2, Zhijiang Zuo1,2

  • 1State Key Laboratory of Precision Blasting, Jianghan University, Wuhan, China.

Frontiers in plant science
|September 29, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种改进的YOLOv8n-seg模型,用于精确的果和茎细分,使机器人能够在自然环境中准确地定位机器人采摘点. 改进后的模型实现了高精度和回忆,为自动化果收获铺平了道路.

关键词:
这就是YOLOv8n-seg.类的类植物.实例细分 实例细分 实例细分采集点定位位置的定位挑选机器人的机器人

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科学领域:

  • 农业机器人农业机器人
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 机器人收获类植物是具有挑战性的,因为小的茎尺寸,背景颜色的相似性,和可变的水果定位.
  • 精确定位采摘点对于高效和有效的类水果自动采摘至关重要.

研究的目的:

  • 开发一个改进的YOLOv8n-seg模型来对类水果和茎进行细分.
  • 通过使用几何约束来实现机器人收获采摘点的准确定位.
  • 增强特征表示和小物体检测,以提高细分精度.

主要方法:

  • 用GhostConv取代标准卷积,以减少模型参数.
  • 集成了一个卷积块注意模块 (CBAM) 和一个小物体检测层.
  • 结合了水果茎位置关系和几何约束,用于茎匹配和最佳采摘点的确定.

主要成果:

  • 果实和茎的召回率达到90.91%,精度为96.04% (果实) 和97.12% (茎).
  • 获得了94.43%的平均精度 (mAP50) 和93.51%的F1得分.
  • 展示了高实时性能,平均检测率为88.38%,可在373.25毫秒内选择点 (支持GPU).

结论:

  • 改进的YOLOv8n-seg模型可靠有效地定位果采摘点.
  • 该研究为推进自动化果收获系统的发展提供了坚实的技术基础.