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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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Extraction: Advanced Methods00:56

Extraction: Advanced Methods

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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
433
Reducing Line Loss01:18

Reducing Line Loss

150
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
150
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
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Force Classification01:22

Force Classification

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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相关实验视频

Updated: Jun 14, 2025

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
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Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

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轻量级玉米叶检测和计数使用改进的YOLOv8

Shaotong Ning1, Feng Tan1, Xue Chen1

  • 1College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China.

Sensors (Basel, Switzerland)
|August 29, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种改进的YOLOv8模型,用于准确计数玉米叶. 改进的方法显著提高了检测的准确性和效率,有助于植物生长评估和育种决策.

关键词:
这是SatrNet的网络.这就是YOLOv8的意义.叶子计数 叶子计数轻量级的轻量级的轻量级的轻量级的玉米玉米玉米是一种

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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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LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement
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LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement

Published on: January 21, 2013

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相关实验视频

Last Updated: Jun 14, 2025

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Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

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LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement
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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:

  • 准确的玉米叶子计数对于评估植物生长和优化种群结构至关重要.
  • 传统的手工方法繁重且容易出现错误,而现有的图像处理技术缺乏准确性和适应性.

研究的目的:

  • 开发一种改进的,轻量级的基于YOLOv8的方法,用于准确检测和计数玉米叶.
  • 增强特征表示和融合能力,以更好地检测玉米生长状态.

主要方法:

  • 该研究提出了一种改进的轻量级YOLOv8模型,该模型包含StarNet骨干和卷积和注意力融合模块 (CAFM).
  • 修改包括在部网络中增强C2f模块与StarBlock,并实施轻量级共享卷积检测头 (LSCD).

主要成果:

  • 改进的模型实现了高性能指标:97.9%的精度,95.5%的回忆率和97.5%的mAP50.
  • 与原来的YOLOv8.8相比,模型大小和参数数量分别减少了39.68%和40.86%,显著减少.

结论:

  • 开发的模型为玉米叶子检测和计数提供了卓越的准确性和效率.
  • 这项技术支持育种,移动检测设备应用和高质量的玉米生长评估的科学决策.