轻松计数线虫:利用人工智能驱动的自动化来提高效率和精度
Kanan K Saikai1,2, Trim Bresilla1, Janne Kool1
1Agrosystems Research, Plant Science Group, Wageningen University and Research, Wageningen, Netherlands.
Frontiers in plant science
|July 12, 2024
概括
现在,通过使用深度学习同时识别和分类多种线虫类型的新工具,自动化线虫计数变得更加实用. 这种用户友好的系统显著提高了定量线虫学研究的准确性和效率.
科学领域:
- 农业科学 农业科学
- 遗体学 遗体学 是一个学科.
- 机器学习 机器学习
背景情况:
- 线虫计数对于定量研究至关重要,但需要大量的劳动和时间.
- 现有的自动化方法专注于单一类对象识别,限制了实际应用.
- 需要能够同时检测和分类多种线虫类型的算法.
研究的目的:
- 开发一个用户友好的图形用户界面 (GUI),用于自动化虫计数.
- 为了能够同时识别和分类多种线虫类别,特别是Meloidogyne spp. 蛋和幼. 这些都是蛋和幼.
- 利用深度学习模型提高线虫种群密度评估的准确性和效率.
主要方法:
- 使用两个成像系统,生成了650张线虫卵的图像和1339张Meloidogyne幼虫的图像.
- 通过使用边界盒和细分技术,注释了8655个蛋和4742个幼.
- 开发了使用YOLOv8x卷积神经网络 (CNN) 架构的深度学习模型.
主要成果:
- 这些模型在识别卵子时达到94%的准确性,在识别Meloidogyne幼时达到93%的准确性.
- 在模型预测和对未见图像的观察之间证明了比0.70大的系数相关性.
- 开发的GUI,结合这些模型,在GitHub上公开提供.
结论:
- 这项研究成功地证明了深度学习模型对实际,自动化线虫计数的潜在实用性.
- 开发的GUI提供了一个用户友好的解决方案,用于同时识别和分类多种线虫类型.
- 这项工作是通用自动化线虫计数系统的基础,鼓励全球合作.
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