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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.
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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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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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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Plants are multicellular eukaryotes with tissue systems made of various cell types that carry out specific functions. Different tissues work together to perform a unique function and form an organ. Organs working together form organ systems. Vascular plants have two distinct organ systems: a shoot system and a root system. The shoot system consists of two portions: the vegetative (non-reproductive) parts of the plant, such as the leaves and the stems, and the reproductive parts of the plant,...
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可视化植物疾病分布和评估深度学习分类模型性能使用YOLOv8

Abdul Ghafar1, Caikou Chen1, Syed Atif Ali Shah2,3

  • 1College of Information Engineering, Yangzhou University, Yangzhou 225009, China.

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概括

本研究引入了一种使用YOLOv8 (你只看一次版本8) 检测植物疾病的新方法. 人工智能模型在图像中准确识别植物疾病,显示实时农业监测的前景.

关键词:
暗网 (DarkNet) 是一个暗网.这就是ResNet ResNet.这就是YOLO v8.卷积神经网络是一种卷积神经网络.植物 疾病 疾病

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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
  • 人工智能的人工智能

背景情况:

  • 植物疾病对全球粮食安全构成重大威胁,需要有效的检测方法.
  • 传统的疾病鉴定可能耗时,需要专家知识.
  • 深度学习的进步为自动化和准确的植物疾病诊断提供了潜力.

研究的目的:

  • 开发和评估一种使用YOLOv8物体检测模型检测植物疾病的新方法.
  • 评估YOLOv8模型在分类各种植物条件中的准确性和稳定性.
  • 探索YOLOv8适用于农业环境中的实时植物疾病监测的适用性.

主要方法:

  • 在植物图像数据集上训练一个定制的YOLOv8模型.
  • 在专门的测试子集上评估模型的性能.
  • 通过使用来自谷歌图像的多样化的未见图像集进一步验证模型的概括性.

主要成果:

  • YOLOv8模型在检测和分类植物疾病方面表现出高准确度.
  • 该模型在训练/测试数据集和未见的真实世界图像上都表现出强的性能.
  • 与现有方法相比,YOLOv8在检测速度和精度方面取得了显著的改进.

结论:

  • 提出的基于YOLOv8的方法对于准确和快速检测植物疾病是有效的.
  • 这种方法在农业早期疾病检测和预防方面具有强大的实际应用潜力.
  • 使用YOLOv8可方便实时监控,有助于改善作物管理和产量.