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Simpson's Rule II

Simpson's Rule II

In warehouse roofing applications, corrugated or curved metal sheets are commonly used to improve structural strength, water drainage, and ventilation efficiency. To accurately estimate material requirements and optimize design parameters, engineers must determine the curved surface area of these sheets. Because the sheet profiles often repeat smoothly along their length, they can be effectively approximated by parabolic curves, enabling the use of numerical integration techniques for area...

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Updated: Jun 20, 2026

Using Flatbed Scanners to Collect High-resolution Time-lapsed Images of the Arabidopsis Root Gravitropic Response

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通过姿势估计进行快速有效的根系表型化.

Elizabeth M Berrigan¹, Lin Wang¹, Hannah Carrillo¹

¹Salk Institute for Biological Studies, La Jolla, CA 92037, USA.

Plant phenomics (Washington, D.C.)

|April 17, 2024

概括

此摘要是机器生成的。

这项研究引入了一种新的,更快的植物表型化方法,使用基于深度学习的地标检测,避免了繁的图像细分. 这种姿势估计方法准确地捕捉了根系拓和特征,注释较少.

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相关实验视频

Last Updated: Jun 20, 2026

Using Flatbed Scanners to Collect High-resolution Time-lapsed Images of the Arabidopsis Root Gravitropic Response

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Published on: January 25, 2014

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

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Published on: August 8, 2017

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

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Published on: March 28, 2025

科学领域:

植物生物学植物生物学
计算机视觉计算机视觉计算机视觉
生物信息学是一种生物信息学.

背景情况:

图像细分是植物表型的标准,但是劳动密集型和容易出错的.
现有的方法需要对训练细分模型进行广泛的数据注释.
来自细分面具的几何特征对面具准确性敏感.

研究的目的:

通过深度学习开发一种无细分的方法来进行植物根表型化.
为了自动检测植物根上的形态标志.
为了实现准确和高效的根特征提取和分析.

主要方法:

利用基于深度学习的里程碑检测和分组 (姿势估计) 使用SLEAP (社会 LEAP估计动物姿势).
应用该方法在多种物种中使用凝圆柱成像系统种植根.
开发了Python库"sleap-roots"用于从姿势数据中提取特征.

主要成果:

姿势估计方法可靠,高效地恢复了根系拓,并具有很高的准确性.
与基于细分的方法相比,使用较少的注释样本并以更快的速度实现了高精度.
姿势衍生的根性特征在下游任务中表现出高的准确性和实用性,例如基因型分类.

结论:

确定了植物表型定位的姿势估计的有效性和优势,为细分提供了更有效,更准确的替代方案.
"睡眠根"库可以直接比较姿势衍生的特征与基于细分的分析.
开发的工具,数据和代码是公开的,以鼓励采用和进一步研究.