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Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins
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使用图像处理和深度学习进行自动种子计数.

Qiuyu Zu1,2, Teng Liu2, Wenpeng Zhu2

  • 1School of Agriculture, Jilin Agricultural Science and Technology College, Jilin City, China.

Frontiers in plant science
|September 15, 2025
PubMed
概括
此摘要是机器生成的。

使用移动应用技术的自动种子计数为农业中使用手工方法提供了更快,更有效的替代方案. 图像处理和深度学习方法为作物研究和育种提供了实际的解决方案,减少了劳动和时间.

关键词:
这是YOLOv5的.深度学习是一种深度学习.图像处理是图像处理的过程.移动应用程序移动应用程序种子计数的种子计数

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科学领域:

  • 农业科学 农业科学
  • 计算机视觉 计算机视觉
  • 生物技术是生物技术.

背景情况:

  • 手动种子计数对于农业研究至关重要,但效率低下,容易出现错误.
  • 微型种子对传统的计数方法提出了特殊的挑战.

研究的目的:

  • 开发和评估用于种子计数的自动化计算机视觉方法.
  • 将这些方法集成到一个用户友好的移动应用程序中.

主要方法:

  • 开发了两个自动化方法:图像处理 (IP) 和深度学习 (DL).
  • 将IP和DL方法集成到种子计数的移动应用程序中.

主要成果:

  • IP方法实现了高精度和节省时间,但需要控制照明.
  • DL方法提供了快速处理 (0.33秒/图像),但与复杂的种子集群不一致的准确性.
  • 这两种方法都比手动计数显著提高了效率.

结论:

  • 通过移动应用程序自动计数种子可以提高实验室和现场应用的效率.
  • 这些技术简化了作物育种,生产和研究的种子计数,减少了人工劳动.