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相关概念视频

Aggregates Classification01:29

Aggregates Classification

346
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...
346
Classification of Illness01:17

Classification of Illness

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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
7.6K
Classification of Systems-I01:26

Classification of Systems-I

215
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:
215
Classification of Systems-II01:31

Classification of Systems-II

177
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,
177
Classification of Signals01:30

Classification of Signals

532
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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Methods of Classification and Identification01:28

Methods of Classification and Identification

37
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
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Enhanced deep learning technique for sugarcane leaf disease classification and mobile application integration.

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通过集体深度学习提高甘疾病分类:使用转移学习技术进行比较研究.

Swapnil Dadabhau Daphal1, Sanjay M Koli2

  • 1Department of E&TC Engineering, G. H. Raisoni College of Engineering & Management, Wagholi, Pune, 412207, Maharashtra, India.

Heliyon
|July 31, 2023
PubMed
概括

这项研究引入了用于甘疾病分类的新深度学习模型,达到86.53%的准确性. 该研究还为农业应用提供了一个新的甘叶病图像数据库.

关键词:
农业 农业 农业 农业深度学习是一种深度学习.疾病的分类疾病的分类.甘数据库中的糖数据库

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科学领域:

  • 农业科学 农业科学
  • 计算机科学 计算机科学
  • 机器学习 机器学习

背景情况:

  • 深度学习为农业挑战提供解决方案,例如疾病检测.
  • 准确的疾病分类对于甘作物管理至关重要.

研究的目的:

  • 开发和评估用于甘叶病疾病分类的深度学习模型.
  • 引入一个新的,自创的甘叶病数据库.

主要方法:

  • 利用转移学习技术 (MobileNet-V2) 和一个拟议的集体深度学习架构.
  • 开发了一个由两个网络组成的堆组合,具有水平智能的空间注意力.
  • 创建了一个新的数据库,包含5个类别的2569张甘叶病图像.

主要成果:

  • 最好的转移学习方法,MobileNet-V2,在最小的参数下实现了84%的准确性.
  • 拟议的整体模型达到86.53%的准确性,具有较少的时代和可接受的参数.
  • 新的数据集为农业人工智能研究提供了宝贵的资源.

结论:

  • 集成深度学习模型显示,甘疾病分类的性能有所改善.
  • 开发的模型和数据集有助于在农业中推进人工智能.
  • 进一步的研究可以利用这一数据集来改善作物疾病管理解决方案.