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深度学习以捕捉植物图像中的叶子形状:通过几何形态测量验证

Ladislav Hodač¹, Kevin Karbstein¹, Lara Kösters¹

¹Department Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany.

The Plant journal : for cell and molecular biology

|October 9, 2024

概括

深度学习 (DL) 模型可以从叶子图像中识别植物物种. 将DL与几何形态学 (GM) 结合起来,有助于理解DL如何捕捉叶子形状变化,改善自动植物表型.

科学领域:

植物学植物学
计算机科学计算机科学
生态生态学生态生态学

背景情况:

自动化植物物种识别依赖于深度学习 (DL) 模型,分析叶子图像.
使用DL复制性地捕捉叶形状变化是具有挑战性的,因为叶形状的变化.
黑盒子是一个黑盒子.
这些模型的性质.

研究的目的:

为了评估DL在捕捉叶子形状变化的有效性.
使用几何形态测量 (GM) 作为一种可解释的人工智能 (XAI) 工具来解释DL特征.
评估DL提取特征与生物学相关的叶形状变异之间的关联.

主要方法:

拍摄了Ranunculus auricomus在现场和草本化后的叶子.
使用神经网络从叶子图像中提取了DL特征.
使用GM数字化了叶子形状,并通过尺寸缩小和共变模型分析了DL特征与GM形状之间的关联.

主要成果:

DL的特点是,在现场和草本化数据集中,通过源种群成功地集群了叶子图像.
在某些DL特征和由GM推断的生物叶形状变化之间发现了显著的关联.
DL特征使叶子能够在R. auricomus物种复合体内被分为形态系基因组.

结论:

简单的现场叶子成像与DL相结合,可复制地捕捉到种群层面的叶子形状变化.
将DL与GM集成提供了对计算机视觉提取的形状信息的关键见解.
这种方法对于可靠的自动化植物表型定型至关重要.

关键词:

这里是Ranunculus auricomus的位置.深度学习是一种深度学习.不可解释的AI 几何形态形态测量是几何形态的.叶子图像叶子图像现型变异的表型变化智能手机在现场成像.

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