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相关实验视频

Updated: May 7, 2025

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
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在使用Dualex测量和机器学习估计米的和效应的缓解.

Peihua Shi1, Yuan Wang2, Congfei Yin1

  • 1Department of Agronomy and Horticulture, Jiangsu Vocational College of Agriculture and Forestry, Jurong, China.

Frontiers in plant science
|December 31, 2024
PubMed
概括

使用Dualex传感器测量黄素含量 (Flav) 和平衡指数 (NBI),准确估计米的状态. 这种方法克服了高含量的叶绿素读数的局限性,改善了作物管理.

关键词:
双轴测量 双轴测量在SHAP分析中,我们分析了SHAP.增量分析增量分析.机器学习是机器学习.平衡指数是指平衡指数.大米的估计.和效应是一种和效应.

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Quantifying Plant Soluble Protein and Digestible Carbohydrate Content, Using Corn Zea mays As an Exemplar
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科学领域:

  • 农业科学 农业科学
  • 植物生理学 植物生理学
  • 遥感 遥感 遥感 遥感

背景情况:

  • 对大米至关重要,但传统的评估方法是劳动密集型的,在高水平时不可靠.
  • 基于叶绿素的测量和,限制了在高条件下对大米的精确气状态的评估.

研究的目的:

  • 通过使用Dualex传感器评估酸盐含量 (Flav) 和平衡指数 (NBI) 来精确估计米的状态.
  • 为了克服在高气环境中的叶绿素测量的和限制.

主要方法:

  • 在不同度下对15种大米进行实地实验.
  • 杜亚莱克斯传感器测量了叶子顶部的叶绿素 (Chl),Flav和NBI.
  • 机器学习模型 (随机森林,XGBoost) 用于度预测.

主要成果:

  • 叶绿素测量显示,在高度下,和效应会产生.
  • 在所有水平上,Flav和NBI仍然敏感,准确地反映了状态.
  • 机器学习模型实现了叶子和植物度的高预测精度 (R2 > 0.82).
  • SHAP分析确定了前两个叶子中的NBI和Flav作为关键预测因素.

结论:

  • 黄酸含量和NBI测量有效克服了叶绿素和度的限制.
  • 将Flav和NBI与机器学习相结合,可以精确地估计米中的.
  • 这种方法为改善水栽培中管理提供了实际解决方案.