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相关概念视频

Classification of Illness01:17

Classification of Illness

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 and...

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相关实验视频

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LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement
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一个基于自动细分和超参数优化的人工子算法用于叶病分类.

Ihtiram Raza Khan1, M Siva Sangari2, Piyush Kumar Shukla3

  • 1Department of Computer Science, Jamia Hamdard, Delhi 110062, India.

Biomimetics (Basel, Switzerland)
|September 27, 2023
PubMed
概括
此摘要是机器生成的。

一种新的深度学习方法,即自动细分和超参数优化人工子算法 (AS-HPOARA),准确地分类植物叶病. 这种方法通过早期检测和尽量减少作物损失来提高农业产量.

关键词:
人工子算法的人工子算法自动细分自动细分自动细分超级参数优化超级参数优化叶病的分类 叶病的分类合成图像是一种合成图像.

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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
  • 人工智能的人工智能

背景情况:

  • 植物病对农业构成重大威胁,造成大量经济损失.
  • 植物疾病的早期检测和分类对于尽量减少传播和提高作物产量至关重要.
  • 深度学习模型需要大数据集来准确的图像分类,经常面临过度匹配的挑战.

研究的目的:

  • 开发一个先进的深度学习模型,以改进植物叶病的分类.
  • 解决过度装配问题,提高植物疾病检测中的分类准确性.
  • 为农业应用引入自动细分和超参数优化人工子算法 (AS-HPOARA).

主要方法:

  • 利用植物村数据集来评估AS-HPOARA方法.
  • 应用Z-score规范化和三个增强技术 (旋转,缩放,翻译) 来平衡和预处理图像.
  • 使用修改后的UNet进行图像细分和基于HPO的ARA进行分类,包括超参数调整.

主要成果:

  • 在10个植物疾病类别中,AS-HPOARA模型实现了99.7%的高分类准确度.
  • 图像增强技术有效地减少了过拟合,提高了分类准确性.
  • 与CGAN-DenseNet121和RAHC_GAN.等现有模型相比,拟的方法显示出更高的性能.

结论:

  • AS-HPOARA显著提高了植物叶病分类的准确性.
  • 开发的算法有效地减轻了过拟合,并改善了模型通用化.
  • 这种方法具有早期疾病检测的潜力,有助于农业可持续性和经济稳定.