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相关概念视频

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: May 21, 2025

Tomato Analyzer: A Useful Software Application to Collect Accurate and Detailed Morphological and Colorimetric Data from Two-dimensional Objects
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Tomato Analyzer: A Useful Software Application to Collect Accurate and Detailed Morphological and Colorimetric Data from Two-dimensional Objects

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基于实例细分的番茄成熟度检测和水果细分.

Jinfan Wei1, Yu Sun1,2, Lan Luo1

  • 1College of Information Technology, Jilin Agricultural University, Changchun, China.

Frontiers in plant science
|May 19, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了ACP-Tomato-Seg,这是一个改进的YOLOv8s-seg模型,用于精确的番茄实例细分,增强机器人在复杂环境中采摘. 这种新方法显著提高了检测和细分西红的准确性,即使有遮.

关键词:
亚太地区 - - 番茄 - - 细分适应性特征提取 适应性特征提取复杂的现场环境 复杂的现场环境多个尺度的特征是多个尺度的特征.自己注意力机制机制.番茄实例细分 番茄实例分割

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Isolation and Biophysical Study of Fruit Cuticles
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Isolation and Biophysical Study of Fruit Cuticles

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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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相关实验视频

Last Updated: May 21, 2025

Tomato Analyzer: A Useful Software Application to Collect Accurate and Detailed Morphological and Colorimetric Data from Two-dimensional Objects
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Tomato Analyzer: A Useful Software Application to Collect Accurate and Detailed Morphological and Colorimetric Data from Two-dimensional Objects

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Isolation and Biophysical Study of Fruit Cuticles
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Isolation and Biophysical Study of Fruit Cuticles

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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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科学领域:

  • 计算机视觉 计算机视觉
  • 机器人技术 机器人技术 机器人技术
  • 农业技术 农业技术

背景情况:

  • 精准农业需要准确的果子轮信息,用于机器人采摘等自动化任务.
  • 复杂的现场条件 (照明,遮蔽,重叠) 挑战现有的水果细分方法.

研究的目的:

  • 开发一种增强的番茄实例细分模型,以支持精确的机器人采摘.
  • 为了提高模型在具有挑战性的环境条件下的强度.

主要方法:

  • 拟议的ACP-Tomato-Seg,是一个改进的YOLOv8s-seg模型,包含自适应和定向特征改进模块 (AOFRM) 和自定义多尺度聚合模块 (CMPRD).
  • 集成了部分自我注意模块 (PSA) 以提高全球上下文和细节提取.
  • 在AOFRM中利用可变形和多方向不对称的卷曲来提取形状和方向特征.
  • 在CMPRD中使用自定义的聚合核,用于多级特征提取,以区分番茄大小和成熟度.

主要成果:

  • 与原来的YOLOv8s-seg相比,ACP-Tomato-Seg取得了显著的改进:mAP50增加了5.6% (边界盒) 和5.8% (面膜),mAP50-95增加了8.3% (边界盒) 和8.5% (面膜).
  • 该模型在公共草数据集 (StrawDI_Db1) 上显示出卓越的性能和概括能力,超过了比较方法.
  • 在一个定制数据集上对1061张涵盖六个成熟度类别的番茄图像进行验证,证实了该方法的有效性.

结论:

  • ACP-Tomato-Seg为番茄实例细分提供了强大而准确的解决方案,解决了诸如闭塞和成熟度变化等挑战.
  • 拟议的方法为精确的果实检测和细分提供了一种有效的方法,这对于推进农业中的机器人采摘至关重要.
  • 该模型的强大性能和通用化能力突出显示了其对现实世界农业应用的潜力.