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一个渐进的特征学习网络用于Cordyceps sinensis图像识别.

Shangdong Liu1, Wenxiang Wu1, Haijun Chen2

  • 1School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.

Sensors (Basel, Switzerland)
|November 27, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的AI方法,即渐进特征学习网络 (PFL-Net),用于准确识别Cordyceps sinensis (C. sinensis) 亚种. PFL-Net实现了高精度,克服了类似亚种形态所带来的挑战.

关键词:
科尔迪塞普斯 (Cordyceps sinensis) 是一种植物.采矿的特点是采矿.图像识别功能 图像识别功能渐进式学习是一种渐进式的学习.

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科学领域:

  • 植物学 植物学
  • 计算机科学 计算机科学
  • 机器学习 机器学习

背景情况:

  • (Cordyceps sinensis) 是一种有价值的草药.
  • 由于高度的形态相似性和有限的表型变异,自动识别C. sinensis亚种是很困难的.

研究的目的:

  • 开发一种用于自动化,细粒度识别C. sinensis亚种的新方法.
  • 通过挖掘多个生物特征来提高C. sinensis识别的准确性和完整性.

主要方法:

  • 提出了进步特征学习网络 (PFL-Net),其中包括空间意识的语义精细化模块 (SSRM) 和多尺度协作感知模块 (MCPM).
  • 引入了频道解 (CD) 损失以增强功能多样性.
  • 为生物识别构建了一个C. sinensis数据集 (CSD).

主要成果:

  • 在中央证券交易所数据集上,PFL-Net取得了94.43%的top-1准确度.
  • 拟议的方法超越了所有关于中央证券交易所和三个基准数据集的现有方法.
  • SSRM和MCPM有效地捕获了多个规模的歧视性特征,并增强了识别完整性.

结论:

  • PFL-Net在细粒度C. sinensis亚种识别方面表现出卓越的表现.
  • 新型模块和CD损失有助于增强功能学习和多样性.
  • 这项工作为C. sinensis生物识别提供了强大的解决方案,并为未来的研究奠定了基础.