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相关概念视频

Light Acquisition02:16

Light Acquisition

8.4K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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Aggregates Classification01:29

Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
314
Classification of Signals01:30

Classification of Signals

432
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
432
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
28.3K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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Force Classification01:22

Force Classification

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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相关实验视频

Updated: Jun 22, 2025

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.
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High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.

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有效的玉米:在资源有限的设备上进行玉米分类的轻量级数据集.

Emmanuel Asante1, Obed Appiah1, Peter Appiahene1

  • 1Department of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani.

Data in brief
|July 4, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了加纳当地玉米种子的新,可访问的数据集,以实现高效,低成本的种子分类. 目标是支持开发能够减少种子分类计算需求和人类努力的工具.

关键词:
卷积神经网络是一个卷积神经网络.图像识别 图像识别 图像识别机器学习是机器学习.机器学习算法 机器学习算法玉米数据集 玉米数据集精准农业 精准农业 精准农业

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

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Micron-scale Phenotyping Techniques of Maize Vascular Bundles Based on X-ray Microcomputed Tomography
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Micron-scale Phenotyping Techniques of Maize Vascular Bundles Based on X-ray Microcomputed Tomography

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相关实验视频

Last Updated: Jun 22, 2025

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.
05:55

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.

Published on: June 16, 2018

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Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
06:41

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

Published on: March 28, 2025

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Micron-scale Phenotyping Techniques of Maize Vascular Bundles Based on X-ray Microcomputed Tomography
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Micron-scale Phenotyping Techniques of Maize Vascular Bundles Based on X-ray Microcomputed Tomography

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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
  • 数据科学数据科学数据科学

背景情况:

  • 超光谱成像和深度学习用于玉米分类,但需要大量的计算资源,限制了嵌入式系统的部署.
  • 高GPU功耗和对当地加纳玉米数据的有限访问给加纳开发高效的分类工具带来了挑战.

研究的目的:

  • 为开发高效的分类工具,创建一个简单,可访问的加纳当地玉米种子数据集.
  • 为了最大限度地降低计算成本,并减少人类参与营销和生产玉米种子的分类.

主要方法:

  • 该研究涉及创建来自加纳的三种本地玉米种子原始和增强图像的数据集.
  • 原始图像数据集包含4,846个图像 (2,211个"坏",2,635个"好"),增强数据集有28,910个图像 (13,250个"坏",15,660个"好").

主要成果:

  • 已经创建了一个加纳当地玉米种子的验证数据集,包括原始和增强图像.
  • 数据集被分为"坏"和"好"质量的种子,经过遗产种子加纳的专家验证.

结论:

  • 开发的数据集有助于创建计算效率高的玉米分类工具.
  • 该资源旨在克服加纳的数据可访问性问题,并减少对种子分类的大量计算能力和人类干预的需求.