Comparison of open-source image-based reconstruction pipelines for 3D root phenotyping of field-grown maize
Comparison of open-source image-based reconstruction pipelines for 3D root phenotyping of field-grown maize
复制标题
用于田间种植玉米 3D 根表型分析的基于开源图像的重建流程的比较
DOI：
10.1002/essoar.10508794.2
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Liu, Suxing
Bonelli
中科院分区：
文献类型：
--
作者：
Liu, Suxing
Bonelli
了解根系特征对于提高水分吸收、增加氮捕获和加速大气中碳固存至关重要。然而，通过高通量表型分析来量化更深的田间生长根的根性状仍然是一个挑战。最近开发的开源方法使用 3D 重建算法从多个 2D 图像构建植物根部的 3D 模型，并可以提取根部性状和表型。这些方法大多数依赖于自动图像定向（运动结构）[1] 和密集图像匹配（多视图立体）算法从 2D 图像生成 3D 点云或网格模型。到目前为止，这些方法应用于田间生长的根时的性能尚未在真实田间条件下生长的 12 种对比玉米基因型的测试板上与常用的开源管道进行比较测试[2-6]。我们从点数、计算时间和模型表面密度方面比较了生成的 3D 点云。这项比较研究深入了解了玉米根表型分析的不同开源管道的性能，并阐明了未来高通量 3D 根表型分析的 3D 模型质量和性能成本之间的权衡。 DOI 识别无法正常工作：https://doi.org/10.1002/essoar.10508794.2
Understanding root traits is essential to improve water uptake, increase nitrogen capture and accelerate carbon sequestration from the atmosphere. High-throughput phenotyping to quantify root traits for deeper field-grown roots remains a challenge, however. Recently developed open-source methods use 3D reconstruction algorithms to build 3D models of plant roots from multiple 2D images and can extract root traits and phenotypes. Most of these methods rely on automated image orientation (Structure from Motion)[1] and dense image matching (Multiple View Stereo) algorithms to produce a 3D point cloud or mesh model from 2D images. Until now the performance of these methods when applied to field-grown roots has not been compared tested commonly used open-source pipelines on a test panel of twelve contrasting maize genotypes grown in real field conditions[2-6]. We compare the 3D point clouds produced in terms of number of points, computation time and model surface density. This comparison study provides insight into the performance of different open-source pipelines for maize root phenotyping and illuminates trade-offs between 3D model quality and performance cost for future high-throughput 3D root phenotyping. DOI recognition was not working: https://doi.org/10.1002/essoar.10508794.2
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DOI：
10.13031/aim.201900806
发表时间：
2019
期刊：
2019 Boston, Massachusetts July 7- July 10, 2019
影响因子：
--
作者：
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