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相关概念视频

Light Acquisition02:16

Light Acquisition

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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.
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相关实验视频

Updated: Jun 28, 2025

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
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图像过,以提高玉米的检测准确度,使用机器学习算法.

Eric Rodene1,2, Gayara Demini Fernando3, Ved Piyush3

  • 1Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583, USA.

Sensors (Basel, Switzerland)
|April 13, 2024
PubMed
概括
此摘要是机器生成的。

机器学习方法使用无人机图像来自动计算玉米中的玉米. 这些工具通过准确估计开花特征来提高作物增强,从而改善植物育种.

关键词:
无人机成像 无人机成像卷积神经网络是一种卷积神经网络.高通量表型化 (high-throughput phenotyping) 是一种高通量表型化方法.图像细分 图像细分机器学习是机器学习.玉米子检测检测器对象检测检测对象检测对象检测

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

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Author Spotlight: Improved Methods for Preparing Transverse Sections and Unrolled Whole Mounts of Maize Leaf Primordia for Fluorescence and Confocal Imaging
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Author Spotlight: Improved Methods for Preparing Transverse Sections and Unrolled Whole Mounts of Maize Leaf Primordia for Fluorescence and Confocal Imaging

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相关实验视频

Last Updated: Jun 28, 2025

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Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
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Author Spotlight: Improved Methods for Preparing Transverse Sections and Unrolled Whole Mounts of Maize Leaf Primordia for Fluorescence and Confocal Imaging
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Author Spotlight: Improved Methods for Preparing Transverse Sections and Unrolled Whole Mounts of Maize Leaf Primordia for Fluorescence and Confocal Imaging

Published on: September 22, 2023

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科学领域:

  • 农业科学 农业科学
  • 植物育种 植物育种
  • 遥感 遥感 遥感 遥感

背景情况:

  • 基于无人机 (UAV) 的图像对于在植物育种中收集时间序列农学数据至关重要.
  • 自动化数据收集和分析对于高效的作物改进计划至关重要.

研究的目的:

  • 开发机器学习方法,使用基于无人机的图像,在图片层面上进行自动化塔塞尔计数.
  • 评估基于对象的计数通过检测 (CBD) 和基于密度的计数通过回归 (CBR) 方法的准确性.

主要方法:

  • 利用空中摄影数据集对233个玉米杂交系进行了利用.
  • 开发并比较CBD和CBR机器学习方法.
  • 采用图像细分来隔离植物,以改善检测.

主要成果:

  • 基于对象 (CBD) 检测实现了过图像 (90%值) 的0.7033的交叉验证预测精度 (r2).
  • 基于密度 (CBR) 的方法显示,未过图像 (MAE为7.99) 的精度最好.
  • 使用启动的过图像 (90%的门) 的MAE (8.65) 略高于未过图像 (8.90).

结论:

  • 开发了精确的机器学习方法,用于在玉米中自动计数玉米.
  • 这些方法有助于精确估计与开花有关的特征.
  • 该方法支持数据驱动的育种决策,以提高作物改进.