Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Light Acquisition02:16

Light Acquisition

8.5K
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.
8.5K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Improving the thermostability of a GH11 xylanase by directed evolution and rational design guided by B-factor analysis.

Enzyme and microbial technology·2020
Same author

Progresses in clinical studies on antiviral therapies for COVID-19-Experience and lessons in design of clinical trials.

Pediatric investigation·2020
Same author

Coordination of Ligand-Protected Metal Nanoclusters and Glass Nanopipettes: Conversion of a Liquid-Phase Fluorometric Assay into an Enhanced Nanopore Analysis.

Analytical chemistry·2020
Same author

Evaluation of Cannabinoids on the Odonto/Osteogenesis in Human Dental Pulp Cells In Vitro.

Journal of endodontics·2020
Same author

The lncRNA <i>UBE2R2-AS1</i> suppresses cervical cancer cell growth <i>in vitro</i>.

Open medicine (Warsaw, Poland)·2020
Same author

Effects of maxillary incisor inclination on dentoalveolar changes in class II division 1 and 2 non-extraction treatment for Caucasian children - A retrospective study using CBCT.

International orthodontics·2020

相关实验视频

Updated: Jul 26, 2025

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
11:49

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

9.4K

基于RGB图像和机器学习方法的豆叶估计.

Xiuni Li1,2,3, Xiangyao Xu1,2,3, Shuai Xiang1,2,3

  • 1College of Agronomy, Sichuan Agricultural University, Chengdu, China.

Plant methods
|June 17, 2023
PubMed
概括

现在可以使用RGB图像和机器学习来准确估计大豆叶的参数. 一个Unet神经网络实现了高细分精度,而随机森林模型在预测叶面积指数等叶状特征方面表现出色.

关键词:
估计 估计 估计叶子参数 叶子参数机器学习 机器学习在 RGB RGB RGB 里面.豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆

更多相关视频

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
06:41

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Published on: March 28, 2025

937
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.5K

相关实验视频

Last Updated: Jul 26, 2025

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
11:49

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

9.4K
Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
06:41

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Published on: March 28, 2025

937
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.5K

科学领域:

  • 农业科学 农业科学
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • RGB 照片提供了一种动态的方法来估计作物生长,这对于了解植物生理学至关重要.
  • 传统的叶子参数测量是费力和耗时的,阻碍了高效的作物育种.
  • 准确估计大豆叶参数对于加速育种计划至关重要.

研究的目的:

  • 开发和评估机器学习模型,使用RGB图像精确估计大豆叶参数.
  • 为了比较不同回归模型的性能来预测叶数,新鲜重量和面积指数.
  • 引入一种用于大豆表型化的新高效技术.

主要方法:

  • 使用Unet神经网络进行图像细分,从RGB图像中分离大豆叶.
  • 开发和比较随机森林,猫提升和简单非线性回归模型用于参数估计.
  • 模型性能的验证使用诸如IOU,PA,RECALL和平均测试预测准确度 (ATPA) 等指标.

主要成果:

  • 联网实现了高大豆图像细分精度,IOU,PA和回忆值分别为0.98,0.99和0.98.
  • 随机森林在预测叶数,新鲜重量和叶面积指数方面表现优于Cat Boost和简单非线性回归.
  • 随机森林模型的ATPA达到73.45% (叶数),74.96% (叶的新鲜重量) 和85.09% (叶面积指数).

结论:

  • Unet神经网络准确地从RGB图像中对大豆进行细分,从而促进后续分析.
  • 随机森林模型表现出强大的概括能力和高准确度,用于估计关键的大豆叶参数.
  • 将先进的机器学习与数字成像集成,为增强大豆表型提供了有效的方法.