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相关概念视频

Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an organic...
Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting the...
Mass Analyzers: Overview01:13

Mass Analyzers: Overview

The mass analyzer is a crucial component of the mass spectrometer. In the ionization chamber, the vaporized sample is bombarded with a high-energy electron beam to generate a radical cation and further fragment into neutral molecules, radicals, and cations. A series of negatively charged accelerator plates accelerate the cations into the mass analyzer. The mass analyzer separates ions according to their mass-to-charge (m/z) ratios and then directs them to the detector. The common types of mass...
Extraction: Advanced Methods00:56

Extraction: Advanced Methods

Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is formed in...
Sample Handling01:02

Sample Handling

Transportation of samples from the collection point to the laboratory, as well as storage and preservation techniques, are crucial for maintaining sample integrity and ensuring accurate and reliable test results.
Samples should be transported carefully from collection points to the laboratory. They should be properly sealed and clearly labeled to prevent cross-contamination. To preserve the sample integrity, optimal temperature conditions during transport are essential. This could involve using...

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相关实验视频

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Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
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PodNet: 在收获前的大豆田中实时细分Pod实例.

Shuo Zhou1, Qixin Sun1,2, Ning Zhang1,3

  • 1Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, China.

Plant phenomics (Washington, D.C.)
|December 19, 2025
PubMed
概括

本研究介绍了PodNet,这是一个新的实例细分模型,用于准确地识别收获前田里的大豆豆. 这一突破使得精确的,非侵入性的表型定型对于推进大豆育种研究至关重要.

关键词:
高通量场表型化高通量场表型化实例细分是指实例的细分.采摘前数据集 采摘前数据集豆是大豆豆的种类之一.

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科学领域:

  • 农业科学 农业科学
  • 计算机视觉 计算机视觉
  • 植物育种 植物育种

背景情况:

  • 对于大豆繁殖来说,非侵入性的表型是非常重要的.
  • 现有的方法仅限于收获后或室内环境,缺乏现实领域的适用性.

研究的目的:

  • 开发一种精确的,非侵入性的方法,从收获前的田间图像中提取大豆豆区域.
  • 为了创建一个强大的实例细分模型,适用于现实世界的现场条件.

主要方法:

  • 使用视频录制和自动选创建数据集的经济有效的工作流程.
  • 使用大视觉模型进行密集的注释,构建20k大豆豆面膜数据集.
  • 开发了PodNet,这是基于YOLOv8的实例细分模型,包含分层原型聚合和U-EMA用于小物体检测.

主要成果:

  • 在一个定制的Pod细分数据集上,PodNet实现了0.786的平均平均准确率.
  • 该模型在没有背景的现场图像上展示了竞争性性能.
  • 波德网可以在边缘计算平台上实时推断.

结论:

  • 波德网 (PodNet) 是收获前大豆田的第一个实例细分模型,提供低成本,高精度的豆提取.
  • 这项技术对于表型分析和从植物到种子水平的跨度表型化至关重要.