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相关概念视频

Multiple Regression01:25

Multiple Regression

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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
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Climate refers to the prevailing weather conditions in a specific area over an extended period. As the saying goes, “Climate is what you expect. Weather is what you get.” Climate is influenced by geographic factors, such as latitude, terrain, and proximity to bodies of water.
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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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Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...
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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
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统计和机器学习模型用于使用天气指数预测特定地点的作物产量.

Ajith S1, Manoj Kanti Debnath2, Karthik R3

  • 1Department of Agricultural Statistics, Uttar Banga Krishi Viswavidyalaya, Cooch Behar, India. ajithagristat@gmail.com.

International journal of biometeorology
|August 31, 2024
PubMed
概括

准确的作物产量预测需要整合天气数据. 非线性机器学习模型,如支持向量回归 (SVR) 和人工神经网络 (ANN),在特定位置的预测方面表现出卓越的性能.

关键词:
人工神经网络的人工神经网络超参数优化优化 超参数优化部分最小平方回归.处罚回归模型中的惩罚回归模型.支持向量的回归.预测收益率的预测

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科学领域:

  • 农业科学 农业科学
  • 环境科学 环境科学
  • 数据科学数据科学数据科学

背景情况:

  • 预测作物产量对于农业利益相关者来说至关重要.
  • 农作物发展与天气变量密切相关,需要气象数据的整合.
  • 气候产出关系表现出显著的局部变化,有利于地区级建模.

研究的目的:

  • 系统地审查统计和机器学习模型,以利用天气因素预测作物产量.
  • 确定常用和高性能模型用于位置和特定作物的产量预测.
  • 探索不同建模方法的适用性,以捕捉复杂的气候产量相互作用.

主要方法:

  • 统计和机器学习模型的系统文献综述.
  • 用气象数据分析常用于作物产量预测的模型.
  • 基于报告的成功率和适用性的模型性能评估.

主要成果:

  • 人工神经网络 (ANN) 和多重线性回归是最常用的模型.
  • 支持向量回归 (SVR) 显示出高成功率,在各种应用中表现良好.
  • 非线性模型,特别是SVR和ANN,表现优于其他模型,表明天气和作物产量之间的复杂非线性关系.

结论:

  • 像SVR和ANN这样的非线性机器学习模型对于作物产量预测非常有效.
  • 模型优化,包括SVR中的超参数调整和ANN中的激活功能/神经元,提高了预测准确性.
  • 综合优化统计和机器学习技术的地区级建模对于精确的,特定位置的作物产量预测至关重要.