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相关概念视频

Aggregates Classification01:29

Aggregates Classification

305
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...
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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...
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Cluster Sampling Method01:20

Cluster Sampling Method

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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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相关实验视频

Updated: Jun 11, 2025

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics
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基于多尺度特征融合和增强的葡萄集群检测.

Jinlin Ma1,2, Silong Xu3, Ziping Ma4

  • 1School of Computer Science and Engineering, North Minzu University, Yinchuan, 750021, China. majinlin@nmu.edu.cn.

Scientific reports
|September 30, 2024
PubMed
概括
此摘要是机器生成的。

这项研究使用修改后的YOLOv7网络增强了葡萄的检测,在复杂的条件下提高了准确性. 新方法为农业计算机视觉应用提供了更高的精度和回忆.

关键词:
功能增强功能增强.功能融合的特点是:葡萄集群检测检测 葡萄集群检测多个尺度的多个尺度.感应场是一个感应场.

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科学领域:

  • 计算机视觉 计算机视觉
  • 农业技术 农业技术
  • 机器学习 机器学习

背景情况:

  • 葡萄集群检测面临的挑战是由于尺度变化,照明变化和在现实世界农业环境中的遮蔽.
  • 现有的方法在复杂和可变的环境中经常难以准确,这会影响产量估计和管理.

研究的目的:

  • 在具有挑战性的农业场景中提高葡萄检测的准确性和稳定性.
  • 开发一个增强的YOLOv7网络,能够处理尺度差异,照明变化和对象遮蔽.

主要方法:

  • 提出了一种新的YOLOv7网络,用于小型目标,采用多尺度特征提取模块 (MSFEM).
  • 引入了采用扩展卷积的感应场增强模块 (RFAM),以改善各种尺度的检测.
  • 集成了一个空间金字塔聚合跨阶段部分连锁加速器 (SPPCSPCF) 模块,用于多级特征融合和更快的训练.
  • 纳入剩余全球关注机制 (ResGAM),以关注关键特征和地区.

主要成果:

  • 在GrappoliV2数据集上获得了93.29%的平均平均精度 (mAP),比标准YOLOv7.5有5.39%的改进.
  • 与基线YOLOv7模型相比,精度增加了2.83%,回忆增加了3.49%,F1得分增加了0.07.
  • 与最先进的方法相比,在各种环境条件下表现出卓越的检测性能和适应性.

结论:

  • 拟议的多级特征融合和增强YOLOv7网络显著提高了葡萄集群检测的准确性.
  • 该方法有效地解决了诸如尺度变化和阻塞等挑战,证明了其适应农业计算机视觉的能力.
  • 这种先进的检测系统为精准农业提供了有前途的解决方案,改善了葡萄种植的监测和管理.