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相关概念视频

Light Acquisition02:16

Light Acquisition

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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement
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豆叶图像数据集注释了叶子尺寸,细分面具和摄像头校准.

Karla Gabriele Florentino da Silva1,2, Paulo Victor de Magalhães Rozatto1, Kaio de Oliveira E Sousa1

  • 1Department of Computer Science, Federal University of Juiz de Fora, Juiz de Fora, MG, 36036-900, Brazil.

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概括

这项研究引入了用于植物科学研究的普通豆叶图像的新数据集. 该数据集有助于开发计算机视觉模型来测量叶子尺寸,这对于了解植物反应至关重要.

关键词:
面积估计 面积估计深度学习是一种深度学习.这是一个信任标记.叶子测量尺寸 叶子测量尺寸语义细分 语义细分是指语义细分.

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科学领域:

  • 植物生理学 植物生理学
  • 计算机视觉 计算机视觉
  • 农业科学 农业科学

背景情况:

  • 叶子尺寸测量对于评估植物健康状况和对环境因素的反应至关重要,如水的可用性,土壤肥力和农药暴露.
  • 准确的叶子测量对于农业研究和开发作物产量和压力的预测模型至关重要.

研究的目的:

  • 为研究目的提供一个全面的普通豆 (Phaseolus vulgaris) 叶片图像数据集,并附有详细的注释.
  • 促进开发先进的深度学习算法,用于精确的叶子尺寸和植物科学中的相关分析.

主要方法:

  • 创建了一个由6981张612个普通豆叶的图像组成的数据集,每个叶子与信托标记一起成像.
  • 标注包括叶子尺寸 (面积,周长,长度,宽度),图像细分,标记姿势和相机校准细节.
  • 数据收集涉及受控的捕获条件,以确保对算法开发的一致性和可用性.

主要成果:

  • 该数据集为612个单独的普通豆叶提供了精心注释的叶子尺寸和图像属性.
  • 包括关于图像细分,标记面积,标记姿势,捕捉条件和摄像头校准的详细信息.

结论:

  • 这一数据集是计算机视觉和植物生理学研究人员的宝贵资源.
  • 它使叶子尺寸的深度学习模型的发展成为可能,有助于改善植物监测和农业实践.