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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.
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Multiple Regression01:25

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Design Example: Aggregate Gradation01:24

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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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Related Experiment Video

Updated: Jun 15, 2025

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
11:49

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

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Yield prediction for crops by gradient-based algorithms.

Pavithra Mahesh1, Rajkumar Soundrapandiyan1

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

Plos One
|August 26, 2024
PubMed
Summary
This summary is machine-generated.

Accurate crop yield prediction using machine learning aids farmers. Categorical Boosting (CatBoost) machine learning model achieved 99.123% accuracy in forecasting crop yields, outperforming LightGBM and XGBoost.

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Area of Science:

  • Agricultural Science
  • Data Science
  • Machine Learning

Background:

  • Accurate crop yield assessment is crucial for farmer income, loss minimization, and strategic agricultural planning.
  • Crop yield prediction is a significant challenge in agriculture, impacting decision-making and policy.
  • Environmental and economic factors influence crop selection and yield.

Purpose of the Study:

  • To evaluate and compare the performance of various machine learning algorithms for crop yield forecasting.
  • To identify the most accurate machine learning model for predicting crop yields based on key parameters.

Main Methods:

  • Developed forecasting models using machine learning algorithms: Categorical Boosting (CatBoost), Light Gradient-Boosting Machine (LightGBM), and eXtreme Gradient Boosting (XGBoost).
  • Utilized parameters including pesticides, rainfall, and average temperature for model training.
  • Calculated Root Mean Square Error (RMSE) and R-squared (R2) values to assess prediction accuracy against observed rice yields.

Main Results:

  • CatBoost achieved the highest precision with an accuracy rate of 99.123%.
  • RMSE and R2 values for CatBoost were 800 (0.24), LightGBM were 737 (0.33), and XGBoost were 744 (0.31).
  • Compared to other algorithms, CatBoost, LightGBM, and XGBoost demonstrated superior accuracy in crop yield prediction.

Conclusions:

  • Machine learning algorithms, particularly CatBoost, show significant promise for accurate crop yield prediction.
  • The study framework provides a reliable method for evaluating ML model performance in agriculture.
  • Accurate yield forecasts can support farmers and policymakers in agricultural commodity management.