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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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Transverse Sectioning of Mature Rice Oryza sativa L. Kernels for Scanning Electron Microscopy Imaging Using Pipette Tips as Immobilization Support
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一个有注释的谷物核图像数据库,用于视觉质量检查.

Lei Fan1,2, Yiwen Ding3, Dongdong Fan3

  • 1Gaozhe Technology, Hefei, 230088, China. lei.fan1@unsw.edu.au.

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|November 8, 2023
PubMed
概括
此摘要是机器生成的。

一个新的机器视觉数据库,GrainSet,为谷物核质量检查提供超过35万张注释图像. 该资源通过为小麦,玉米,和大米提供详细的视觉数据,帮助研究智能农业,粮食储存和贸易.

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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:

  • 谷物核的视觉质量检查对于贸易,储存和农业至关重要.
  • 现有的数据集缺乏先进机器视觉应用所需的规模和细节.
  • 准确的谷物评估会影响粮食安全和经济价值.

研究的目的:

  • 介绍GrainSet,一个大规模的基于机器视觉的数据库,用于谷物内核视觉质量检查.
  • 提供全面的注释,包括形态,物理尺寸,重量和损伤类别.
  • 为谷物分析中的深度学习模型建立一个基准.

主要方法:

  • 从全球不同地区收集了超过35万个小麦,玉米,和大米的单核图像.
  • 使用定制设备,配备高分辨率光学传感器,用于表面数据捕获.
  • 集成的专家注释用于采样信息和质量属性,包括损坏和不健全的类别.
  • 实施了一个标准的深度学习模型,用于初始分类的基准测试.

主要成果:

  • 开发了GrainSet,一个超过35万个注释单核图像的数据库.
  • 捕获了四种主要谷物谷物的详细表面信息和经过专家验证的质量评估.
  • 使用数据集展示了深度学习模型用于谷物分类的潜力.

结论:

  • 在谷物质量评估中,GrainSet为推进机器视觉提供了宝贵的资源.
  • 该数据库将支持自动检查,储存指导和智能农业方面的研究.
  • 有助于提高粮食贸易和管理的准确性和效率.