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Fruit Development, Structure, and Function

Fruit Development, Structure, and Function

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: Jun 30, 2025

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

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

Published on: March 16, 2010

EasyDAM_V4:基于引导GAN的跨物种数据标签,用于检测具有显著形状差异的水果.

Wenli Zhang¹, Yuxin Liu¹, Chenhuizi Wang¹

¹Information Department, Beijing University of Technology, Beijing 100022, China.

Horticulture research

|March 15, 2024

概括

这项研究介绍了EasyDAM_V4,一种使用Across-CycleGAN的改进自动水果标签方法. 它有效地将水果图像翻译成不同的形状,改进智能果园AI模型培训.

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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Fruit Volatile Analysis Using an Electronic Nose

Fruit Volatile Analysis Using an Electronic Nose

Published on: March 30, 2012

相关实验视频

Last Updated: Jun 30, 2025

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

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

Published on: March 16, 2010

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Fruit Volatile Analysis Using an Electronic Nose

Fruit Volatile Analysis Using an Electronic Nose

Published on: March 30, 2012

科学领域:

农业技术农业技术
计算机视觉计算机视觉
机器学习机器学习

背景情况:

智能果园依赖于高性能果实检测,这需要大型标记数据集.
目前用于自动标签的方法与表现出显著形状变化的水果作斗争.
手动数据标签是昂贵和耗时的,阻碍了农业人工智能发展.

研究的目的:

提出一个改进的自动水果标签方法,EasyDAM_V4,以解决跨领域水果图像翻译的局限性.
通过翻译形状,纹理和颜色等表型特征,有效减少域差异.
增强自动标签的适用性,以显著的形状差异的水果.

主要方法:

使用一个改进的水果自动标签方法,EasyDAM_V4.4.
引入跨周期GAN水果翻译模型,用于源和目标水果图像之间的跨越翻译.
在梨果 (源域) 和皮塔亚,茄子和黄瓜 (目标域) 上验证该方法,具有很大的表型差异.

主要成果:

EasyDAM_V4 展示了实质性的交叉水果形状转换能力.
取得的平均标签准确率为皮塔亚的87.8%,茄子的87.0%,黄瓜的80.7%.
有效地减少了水果之间具有显著形状差异的域差异.

结论:

EasyDAM_V4 方法显著改善了自动水果标签,即使在域之间存在很大的形状差异.
这项研究通过降低数据标签成本和改进模型培训来增强人工智能在智能果园中的实际应用.
跨周期GAN模型为农业计算机视觉任务中的跨域图像翻译提供了强大的解决方案.