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相关概念视频

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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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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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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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异常的表型缺陷使用可解释的机器学习增强计算机视觉检测朱.

Luwei Zhang1, Yan Chen2, Xiangyun Guo3

  • 1College of Engineering, China Agricultural University, Beijing, China.

Journal of food science
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概括

通过使用先进的成像技术去除有缺陷的水果,提高了朱果的质量. 本研究介绍了精确的方法来分类朱的大小和识别缺陷,提高收获后的价值.

关键词:
异常的表型缺陷异常的表型缺陷.计算机视觉 计算机视觉可以解释的机器学习图像处理是图像处理的过程.

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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:

  • 朱果容易受到环境压力的缺陷,影响市场价值.
  • 收获后分类对于消除异常表型和增加附加值至关重要.

研究的目的:

  • 开发和评估精确尺寸分类和果果的缺陷检测方法.
  • 为了比较机器学习模型来分类各种柔果表型.

主要方法:

  • 改进的最大水平直径线性回归 (MHD-LR) 方法用于大小分级.
  • 建立了一个缺陷检测方法来分类七种朱树表型.
  • 包括SVMDT,逻辑回归,BPNN和LSTM在内的机器学习模型被训练和评估.
  • 使用线性插值进行数据增强,以扩展数据集.

主要成果:

  • MHD-LR模型在尺寸分级方面实现了95%的精度,误差为0.95毫米.
  • 缺陷检测方法准确地分类了七种不同的朱树表型.
  • 在测试的模型中,SVMDT模型显示了最高的分类准确性 (99.57%) 和可解释性.
  • 数据增强有效地扩展了数据集,最小的差异.

结论:

  • 开发的MHD-LR和缺陷检测方法提供了精确的工具,用于收获后的朱果分类.
  • 该SVMDT模型提供了一个高度准确和可解释的解决方案,用于分类朱树的表型.
  • 这项研究为改善收获后朱中异常表型缺陷的精确分类提供了新的方法.