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相关概念视频

Light Acquisition02:16

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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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Like all living organisms, plants require organic and inorganic nutrients to survive, reproduce, grow and maintain homeostasis. To identify nutrients that are essential for plant functioning, researchers have leveraged a technique called hydroponics. In hydroponic culture systems, plants are grown—without soil—in water-based solutions containing nutrients. At least 17 nutrients have been identified as essential elements required by plants. Plants acquire these elements from the...
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Light plays a significant role in regulating the growth and development of plants. In addition to providing energy for photosynthesis, light provides other important cues to regulate a range of developmental and physiological responses in plants.
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Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
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基于计算机视觉的植物表型:一个全面的调查调查.

Talha Meraj1, Muhammad Imran Sharif1, Mudassar Raza1

  • 1Department of Computer Science, COMSATS University Islamabad Wah Campus, Wah Cantt 47040, Pakistan.

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概括

计算机视觉系统对于植物表型定型至关重要,以监测各种环境中的植物特征和生产力. 本综述讨论了用于自动化植物分析的数据收集,细分和分类的当前挑战和解决方案.

关键词:
机器学习是机器学习.现型化 (Phenotyping) 是一种表现方式.植物生物学 植物生物学

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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
  • 植物生物学 植物生物学

背景情况:

  • 全球人口不断增长需要加强粮食生产,并了解植物特征在不同环境中的变化.
  • 手动监测单个植物特征是劳动密集型的,对于大规模的育种计划来说是不切实际的.
  • 计算机视觉为客观,可扩展的植物表型和分析提供了解决方案.

研究的目的:

  • 审查目前基于计算机视觉的植物表型化方法.
  • 识别植物分析数据收集,细分和分类方面的挑战和局限性.
  • 突出现有解决方案及其在解决数据限制方面的缺陷.

主要方法:

  • 对植物表型化中的计算机视觉应用现有文献的审查.
  • 讨论传统和现代的细分和分类技术.
  • 分析数据收集策略及其相关挑战.

主要成果:

  • 采用了各种数据收集方法,每个方法都有其局限性.
  • 讨论了传统的细分和分类方法.
  • 目前的计算机视觉解决方案对于数据限制并不完全充足.

结论:

  • 使用计算机视觉进行自动化植物表型识别对于高效的作物改进至关重要.
  • 在数据采集,质量和模型通用性方面仍然存在重大挑战.
  • 需要进一步的研究,以开发强大的和真正的解决方案,植物表型的挑战.