深度学习的应用用于胃口的分析:对当前方法和未来方向的审查
Jonathon A Gibbs1, Alexandra J Burgess1
1Agriculture and Environmental Sciences, School of Biosciences, University of Nottingham Sutton Bonington Campus, Loughborough LE12 5RD, UK.
Journal of experimental botany
|May 8, 2024
概括
深度学习 (DL) 模型通过自动化微观图像的口腔分析来加速植物表型. 这次审查强调了DL管道,共同的网络结构和研究的物种,促进了植物生理学研究的更广泛采用.
科学领域:
- 植物科学 植物科学
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 胃膜对于植物的气体交换至关重要,影响生理学和新陈代谢.
- 护卫细胞形态和口腔密度决定了气体交换能力.
- 从显微镜图像中手动分析口腔特征是耗时的.
研究的目的:
- 审查深度学习 (DL) 在植物口腔分析中的应用.
- 讨论植物表型化DL管道的变化.
- 突出未来对优化植物科学中DL吸收的需求.
主要方法:
- 对DL用于口腔分析的已发表研究的文献综述.
- 分析数据采集,预处理,DL架构和评估方法.
- 识别共同的网络结构,植物物种和测量.
主要成果:
- 深度学习模型为口腔分析提供了更高的吞吐量和准确性.
- 使用各种DL管道和网络架构.
- 研究涉及多种植物物种,分析各种口腔特征.
结论:
- DL为高通量工厂表型化提供了一个强大的工具.
- 数据集的标准化和模型的概括是未来优化的关键.
- 需要仔细考虑图像数据的限制,以推断生理功能.
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