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相关概念视频

Cluster Sampling Method01:20

Cluster Sampling Method

11.8K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Extraction: Advanced Methods00:56

Extraction: Advanced Methods

436
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...
436
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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Aggregates Classification01:29

Aggregates Classification

310
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
310
Classification of Leukocytes01:30

Classification of Leukocytes

1.8K
Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
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Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

1.3K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
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相关实验视频

Updated: Jun 17, 2025

Automated Quantification of Synaptic Fluorescence in C. elegans
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Automated Quantification of Synaptic Fluorescence in C. elegans

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基于YOLOv5的轻量松花集群检测.

Hui Guo1,2, Tianlun Wu3,4, Guomin Gao3,4

  • 1College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi, 830052, China. gh97026@126.com.

Scientific reports
|August 10, 2024
PubMed
概括

这项研究介绍了Safflower-YOLO (SF-YOLO),这是一个改进的模型,用于在田野中检测红杉. SF-YOLO提高了自动化农业系统的准确性和效率.

关键词:
注意力机制和控制机制和控制机制深度学习是一种深度学习.轻量化 轻量化 轻量化 轻量化 轻量化权重的特征融合重量.

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From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
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From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
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科学领域:

  • 农业工程 农业工程
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 有效的松花检测对于导航和收获等自动化农业系统至关重要.
  • 目前的方法与小花大小,密集分布和复杂的现场条件作斗争,导致精度低和计算成本高.

研究的目的:

  • 开发一个改进的树目标检测模型,树-YOLO (SF-YOLO),提高农业应用的准确性和效率.

主要方法:

  • 在骨干网络中实现了Ghost_conv和CBAM注意力机制,以提高效率和特征提取.
  • 引入了组合损失函数和K-means集群箱,以更好地适应多尺度和更快地趋同.
  • 应用数据增强 (高斯模糊,噪音,利,频道混合) 以提高稳定性.

主要成果:

  • 与YOLOv5s.相比,SF-YOLO减少了GFlops的16.6% (13.2G) 和Params的23.9% (5.34M) 与YOLOv5s.相比,SF-YOLO减少了GFlops的16.6% (13.2G) 和Params的23.9% (5.34M).
  • 在mAP0.5中实现了1.3%的增加,达到95.3%的准确性.
  • 在复杂的农业环境中表现出卓越的性能.

结论:

  • SF-YOLO显著提高了松花检测准确性和计算效率.
  • 该模型为在树种植中开发自主视觉导航和非破坏性收获技术提供了有价值的参考.