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相关概念视频

Methods of Classification and Identification01:28

Methods of Classification and Identification

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Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
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相关实验视频

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Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
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使用机器学习预测算法识别和估计面包小麦基因型的存储.

Ehsan Rabieyan1, Reza Darvishzadeh1, Hadi Alipour2

  • 1Department of Plant Production and Genetics, Urmia University, Urmia, Iran.

Plant methods
|October 18, 2023
PubMed
概括
此摘要是机器生成的。

早期的小麦寄宿可以使用机器学习来预测. 随机森林 (RF) 模型准确地估计了宿舍,有助于精确,非破坏性的监测和管理策略,以提高作物产量.

关键词:
图像处理 图像处理住宿 提供住宿.机器学习 机器学习随机的森林随机的森林小麦小麦小麦小麦的小麦.

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Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
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科学领域:

  • 农业科学 农业科学
  • 植物育种 植物育种
  • 机器学习应用 机器学习应用

背景情况:

  • 麦子存放 (茎曲) 显著降低作物产量和质量.
  • 早期识别耐寄存基因型对于作物改进至关重要.
  • 图像处理和机器学习为非破坏性特征评估提供了潜力.

研究的目的:

  • 为了确定优越的小麦基因型,以产生耐药性.
  • 为了比较多重线性回归 (MLR),支向量回归 (SVR),人工神经网络 (ANN) 和小麦寄存的随机森林回归 (RF) 的预测精度.
  • 评估机器学习模型在早期住宿预测方面的潜力.

主要方法:

  • 在两个作物季节的田间条件下种植了228个小麦加入.
  • 采用了两个复制的alpha-lattice实验设计.
  • 使用图像处理对每块土地上20个孤立植物进行植物特征测量.

主要成果:

  • 住宿分数指数 (LS) 与工厂高度,节点数和内部节点长度有很强的正相关性.
  • 与ANN和SVR相比,随机森林 (RF) 算法在预测小麦存放方面表现出更高的准确性 (R2=0.887训练,R2=0.768测试).
  • 射频表现出强大的性能,与ANN相美,优于SVR.

结论:

  • 射频模型作为一个有价值的预测工具,用于估计在田间条件下的小麦存放.
  • 这种方法支持准确的,非破坏性的存储监控.
  • 调查结果可以为管理策略提供信息,以减轻对小麦生产的住宿影响.