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相关概念视频

Classification of Signals01:30

Classification of Signals

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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...
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Plant Breeding and Biotechnology01:59

Plant Breeding and Biotechnology

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Crop cultivation has a long history in human civilization, with records showing the cultivation of cereal plants beginning at around 8000 BC. This early plant breeding was developed primarily to provide a steady supply of food.
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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...
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Classification of Systems-II01:31

Classification of Systems-II

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Classification of Systems-I01:26

Classification of Systems-I

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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How Data are Classified: Categorical Data01:11

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
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相关实验视频

Updated: Jul 25, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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豆类图像数据集用于分类.

Wei Lin1,2, Youhao Fu1, Peiquan Xu1,3

  • 1Nanjing Agricultural University, Nanjing, China.

Data in brief
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概括
此摘要是机器生成的。

现在可以获得一个新数据集,包含5000多张大豆种子图像,分为五个质量级别. 该资源有助于开发大豆分类和质量评估的自动化系统.

关键词:
卷积神经网络是一种卷积神经网络.图像数据集是一组图像数据集.图像处理 图像处理豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆

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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:

  • 对农业经济来说,大豆质量评估至关重要.
  • 自动化分类系统需要大量,多样化的数据集.
  • 现有的数据集可能缺乏足够多样化的种子质量缺陷.

研究的目的:

  • 为了引入单个大豆种子图像的综合数据集.
  • 促进自动化大豆分类和质量评估方面的研究.
  • 为农业中的图像处理算法提供一个基准.

主要方法:

  • 收集并策划了超过5000张大豆种子图像的数据集.
  • 种子分为五类:完整的,不成熟的,皮肤受损的,斑点的和破碎的.
  • 利用图像处理算法对单个种子进行细分,精度>98%.

主要成果:

  • 一个包含每类别1000多张图像的数据集,涵盖五个不同的大豆种子质量等级.
  • 从较大的图像 (3072x2048像素) 中成功分割单个大豆种子 (227x227像素).
  • 对于单个大豆种子,已经证明了高细分精度 (>98%).

结论:

  • 这一数据集对于训练用于大豆质量评估的机器学习模型非常有价值.
  • 这种资源可以促进自动化农业检查系统的发展.
  • 该数据集支持进一步研究用于作物质量评估的图像分析.