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相关概念视频

Masking and Demasking Agents01:19

Masking and Demasking Agents

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EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
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Color Vision01:24

Color Vision

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Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.
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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: Jul 27, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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预测和可视化果颜色转换使用深度面具引导生成网络.

Zehan Bao1, Weifu Li1,2, Jun Chen1,2

  • 1College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.

Plant phenomics (Washington, D.C.)
|June 9, 2023
PubMed
概括

预测皮的颜色转换现在可以通过新的AI框架来实现. 这项技术通过准确预测水果发育阶段,有助于作物管理和收获计划.

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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:

  • 果皮肤的颜色是水果成熟和发育的关键指标.
  • 准确监测和预测皮肤颜色变化对于优化作物管理和收获时间至关重要.

研究的目的:

  • 开发和验证一个高精度的工作流程,用于预测和可视化皮颜色转换.
  • 创建一个能够预测未来各种时间点皮肤颜色的多功能模型.

主要方法:

  • 一个新的框架,将视觉突出与深度学习相结合,包括细分,生成和丢失网络.
  • 在单个模型中融合图像特征和时间信息,以预测随时间变化的颜色变化.
  • 在颜色转换过程中开发了一套数据集,其中包括来自107个腹子的7,535张图像.

主要成果:

  • 语义细分网络实现了0.9694.4的工会平均交叉点的平均分数.
  • 生成网络显示出高图像质量和相似性,峰值信号噪声比为30.01.
  • 该模型的预测与人类的感知一致,并通过定量指标得到验证.

结论:

  • 拟议的AI框架准确地预测和可视化皮颜色转换.
  • 该模型的效率和准确性适用于现实应用,包括移动应用.
  • 该方法适用于预测其他水果作物的颜色变化.