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相关概念视频

End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
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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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Prediction Intervals01:03

Prediction Intervals

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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.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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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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Improving Translational Accuracy02:07

Improving Translational Accuracy

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Design Example: Aggregate Gradation

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The right type and quality of aggregates are crucial for concrete as they significantly influence its properties, mix proportions, and cost-effectiveness. If different sources are available for sand, the commonly used fine aggregate in concrete, the selection of sand is primarily based on its gradation.
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相关实验视频

Updated: Jun 15, 2025

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
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Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

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用基于梯度的算法预测作物的产量.

Pavithra Mahesh1, Rajkumar Soundrapandiyan1

  • 1School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, India.

PloS one
|August 26, 2024
PubMed
概括
此摘要是机器生成的。

使用机器学习的准确作物产量预测有助于农民. 分类提升 (CatBoost) 机器学习模型在预测作物产量方面实现了99.123%的准确性,表现优于LightGBM和XGBoost.

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科学领域:

  • 农业科学 农业科学
  • 数据科学数据科学数据科学
  • 机器学习 机器学习

背景情况:

  • 准确的作物产量评估对于农民收入,损失最小化和战略农业规划至关重要.
  • 预测作物产量是农业的一个重大挑战,影响决策和政策.
  • 环境和经济因素影响作物选择和产量.

研究的目的:

  • 评估和比较各种机器学习算法用于作物产量预测的性能.
  • 根据关键参数确定最准确的机器学习模型来预测作物产量.

主要方法:

  • 使用机器学习算法开发了预测模型:分类增强 (CatBoost),光梯度增强机器 (LightGBM) 和极端梯度增强 (XGBoost).
  • 使用的参数包括农药,降雨量和模型训练的平均温度.
  • 计算了根平均平方误差 (RMSE) 和R平方 (R2) 值,以根据观察到的水产量评估预测的准确性.

主要成果:

  • CatBoost实现了最高的精度,准确率为99.123%.
  • 对于CatBoost的RMSE和R2值为800 (0.24),LightGBM为737 (0.33),XGBoost为744 (0.31),而这些值均为800 (0.24),LightGBM为737 (0.33),而XGBoost则为744 (0.31).
  • 与其他算法相比,CatBoost,LightGBM和XGBoost在作物产量预测方面表现出卓越的准确性.

结论:

  • 机器学习算法,特别是CatBoost,对准确的作物产量预测有很大的前景.
  • 该研究框架提供了一种可靠的方法来评估ML模型在农业中的性能.
  • 准确的产量预测可以支持农民和政策制定者在农业商品管理.