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相关实验视频

Updated: Jul 27, 2025

Author Spotlight: A High-Resolution, Single-Grain, In Vivo Pollen Hydration Bioassay for Arabidopsis thaliana

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Published on: June 30, 2023

使用机器学习测量花粉发芽率的简单方法.

Akira Yamazaki¹, Ao Takezawa², Kyoka Nagasaka²

¹Faculty of Agriculture, Kindai University, Nara, 631-8505, Japan. yamazaki@nara.kindai.ac.jp.

Plant reproduction

|June 6, 2023

概括

这项研究开发了一种人工智能模型,以准确测量植物花粉发芽率,有助于理解植物繁殖和识别各种物种相关基因.

关键词:

无生物应激应激是一种非生物应激.胡胡胡胡胡显微镜显微镜显微镜对象检测检测对象检测对象检测这就是Yolov5的意义.

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科学领域:

植物生物学植物生物学
计算生物学计算生物学
遗传学是一个遗传学.

背景情况:

花粉发芽对于植物繁殖至关重要,但其测量是劳动密集型的.
像高温一样,非生物应激会对花粉发芽和植物繁殖成功产生负面影响.
精确评估花粉活力对于植物育种和农业研究至关重要.

研究的目的:

使用机器学习开发一种自动化方法来测量花粉发芽率.
创建一个能够区分发芽花粉和非发芽花粉的模型.
为了使得花粉发芽的高通量分析,用于遗传研究.

主要方法:

使用Yolov5机器学习包进行转移学习.
在 (Capsicum annuum) 花粉图像上训练了一个模型,比较640px和320px的图像分辨率.
验证了模型在估计花粉发芽率和检测相关遗传区域方面的准确性.

主要成果:

一个基于Yolov5的模型准确地检测了发芽和非发芽的花粉,640px的图像产生了更高的准确性.
该模型成功估计了C. chinense.先前研究的F2种群中的花粉发芽率.
通过全基因组关联研究确定的关联基因区域使用模型预测的特征重新检测到.
该模型对,西红,卜和草的花粉也表现出类似的准确性,这表明其广泛适用性.

结论:

由人工智能驱动的方法显著提高了花粉发芽率测量的效率和准确性.
这种模型有助于基因分析,识别控制不同植物物种花粉发芽的基因.
这些发现对旨在在压力条件下增强生殖成功的植物育种计划有影响.