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相关概念视频

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...
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Fruits form from a mature flower ovary. As seeds develop from the ovules contained within, the ovary wall undergoes a series of complex changes to form fruit. In some fruits, such as soybeans, the ovary wall dries; in other fruits, such as grapes, it remains fleshy. In some cases, organs other than the ovary contribute to fruit formation; such fruits are called accessory fruits.
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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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.
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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.
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DBA-ViNet:一种有效的深度学习框架,用于使用可解释AI检测和分类水果疾病.

Saravanan Srinivasan1, Lalitha Somasundharam2, Sukumar Rajendran3

  • 1Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, 600089, India.

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一个新的双分支注意引导视觉网络 (DBA-ViNet) 模型准确地识别了果,瓜子,果,石榴和的水果疾病. 这种计算机视觉方法为智能农业和作物健康监测提供了高精度.

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科学领域:

  • 计算机视觉 计算机视觉
  • 农业技术 农业技术
  • 植物病理学 植物病理学

背景情况:

  • 准确地识别水果中的疾病对农业生产率和粮食安全至关重要.
  • 现有的计算机视觉模型在有效整合全球和本地特征以精确检测疾病方面面临挑战.
  • 智能农业需要强大的自动化系统,需要先进的图像分析技术.

研究的目的:

  • 开发和评估一种新的计算机视觉模型,即双分支注意引导视觉网络 (DBA-ViNet),用于识别和分类多种水果类型的疾病.
  • 将DBA-ViNet的性能与先进的预训练卷积神经网络 (ConvNet) 模型进行比较.
  • 通过可视化技术提高模型预测的可解释性和可靠性.

主要方法:

  • 利用开源数据集的水果疾病图像 (果,瓜子,果,石榴,子),分为培训,验证和测试集.
  • 实施5倍交叉验证以确保模型的通用性和稳定性.
  • 基于基准的Swin变压器 (ST),EfficientNetV2,ConvNeXt,YOLOv8和MobileNetV3,并介绍了拟议的DBA-ViNet,用于集成特征提取的双分支架构. 使用Grad-CAM进行可视化.

主要成果:

  • DBA-ViNet模型实现了卓越的性能,测试准确率为99.51%,特异性为99.42%,回忆率为99.61%,精度为99.30%,F1得分为99.45%.
  • 在所有评估指标上,DBA-ViNet的表现超过了所有基准的最先进模型.
  • Grad-CAM可视化证实,DBA-ViNet准确地关注疾病特异性症状,提高了模型的透明度.

结论:

  • 拟议的DBA-ViNet架构在检测水果疾病方面表现出高度的准确性和可靠性.
  • 通过双分支注意力机制整合全球和本地特征提取是对分类任务有效的.
  • DBA-ViNet显示出在智能农业和自动化作物健康监测系统中实际应用的巨大潜力.