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推进根系的超光谱成像技术:宏观和微观图像采集和分类的新管道

Corine Faehn¹, Grzegorz Konert^2,3, Markku Keinänen^2,4,5

¹Department of Arctic and Marine Biology, The Arctic University of Norway, 9037, Tromsø, Norway. corine.a.faehn@uit.no.

|November 11, 2024

概括

超光谱成像 (HSI) 使用随机森林模型可靠地区分植物根和土壤,达到88-91%的准确性. 这种方法有助于监测根生物量,了解植物适应环境条件.

科学领域:

植物科学植物科学
农业科学农业科学
环境科学环境科学

背景情况:

了解对根生长和健康的环境影响对于农业和环境管理至关重要.
超光谱成像 (HSI) 提供植物组织的非破坏性分析,但在根系和根土界面方面未得到充分利用.
缺乏用于HSI获取和根研究数据分析的标准化指南.

研究的目的:

在宏观到微观尺度上研究HSI技术来分析根茎箱生长的根系.
评估图像采集参数和数据处理对根,土壤和根-土壤接口的光谱特征的影响.
将分类方法 (SAM,K-Means) 和机器学习方法 (RF,SVM) 进行比较,用于自动根系统图像分类.

主要方法:

在各种配置中使用imec VNIR SNAPSCAN摄像头用于HSI.
专注于三种具有独特根架构的草类物种.
对比光谱角度映射器 (SAM),K-Means集群,随机森林 (RF) 和支持向量机 (SVM) 进行分类.

主要成果:

用SAM分类和减少波长训练的随机森林 (RF) 模型在区分根和土壤方面达到88-91%的准确性.
根-土壤接口没有明确解决,但改善了根-土壤区分.
该方法突出了由于配置,获取设置和物种的光谱差异,促进了根生物质监测.

结论:

解决了HSI获取和数据处理用于根系统分析的关键挑战.
在根系研究中为更广泛的VNIR HSI应用奠定了基础.
提供数据分析管道作为基于Python的工具,用于半自动分析根土HSI数据.

关键词:

生物质估计生物质估计格拉米诺基的作用是什么?图像分析图像分析根系的表型化是根系的表型化.根-土壤界面的接口这就是VNIR SNAPSCAN.

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