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Fruit Development, Structure, and Function01:58

Fruit Development, Structure, and Function

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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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相关实验视频

Updated: Jan 10, 2026

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
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Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

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包装的水果和蔬菜视觉分类和细分的基准标准.

Svetlana Illarionova1, Sergey Nesteruk2, Tatiana Elina3

  • 1Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30, bld. 1, 121205, Moscow, Russia. s.Illarionova@skoltech.ru.

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|November 29, 2025
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概括
此摘要是机器生成的。

创建了一个新的数据集,用于在零售自动化中识别水果和蔬菜. 它具有多样化的产品,注释和细分口罩,以改善视觉识别系统.

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From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
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科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 零售自动化 零售自动化

背景情况:

  • 目前的零售自动化数据集缺乏多样性和全面的注释,阻碍了强大的视觉识别系统的开发.
  • 准确识别和计数水果和蔬菜对于零售业的库存管理和自动化结账系统至关重要.

研究的目的:

  • 引入用于零售自动化中的视觉识别的新型大规模数据集,特别关注水果和蔬菜.
  • 提供包括多种物种,品种,包装类型和详细注释在内的综合资源,以推进这一领域的研究.

主要方法:

  • 在多个零售地点收集了超过10万张图像的37万种水果和蔬菜物品.
  • 带有标注的图像,包含对象数量,总重量,并为样本子集提供了细分面具.
  • 从多个角度捕获图像,以增强数据多样性.

主要成果:

  • 该数据集包含34个物种和65种水果和蔬菜品种,分类均衡.
  • 提供了零射击/监督分类,实例细分和对象计数任务的基线结果.
  • 包括包装和背景对模型性能影响的分析.

结论:

  • 新的数据集解决了现有资源的局限性,为训练视觉识别模型提供了更加多样化和详细的数据集.
  • 预计这项资源将加速用于现实世界零售自动化应用的多任务模型的开发.
  • 该数据集有助于研究包装等现实世界的变化对自动化系统的影响.