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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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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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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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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Regression Analysis

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Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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An ensemble deep learning approach for predicting cocoa yield.

Sunday Samuel Olofintuyi1, Emmanuel Ajayi Olajubu2, Deji Olanike3

  • 1Department of Computer Science, Achievers University, Owo, Nigeria.

Heliyon
|April 24, 2023
PubMed
Summary

This study introduces an efficient deep learning model for cocoa yield prediction, combining Convolutional Neural Network and Recurrent Neural Network (CNN-RNN) with Long Short Term Memory (LSTM). The model demonstrated superior accuracy compared to traditional methods, improving agricultural planning.

Keywords:
CNN-RNN with LSTMCocoa yield predictionEnsemble modelMachine learning

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Area of Science:

  • Agricultural Science
  • Computer Science
  • Data Science

Background:

  • Crop yield prediction is crucial for agricultural planning and policy-making.
  • Traditional statistical models for crop yield prediction face challenges like inaccuracy and time inefficiency.
  • Deep learning and machine learning offer advanced pattern extraction from large datasets for improved prediction.

Purpose of the Study:

  • To propose an efficient deep learning technique for cocoa yield prediction.
  • To develop a hybrid Convolutional Neural Network and Recurrent Neural Network (CNN-RNN) model integrated with Long Short Term Memory (LSTM).
  • To address the limitations of previous crop yield prediction models.

Main Methods:

  • An ensemble deep learning approach using CNN-RNN with LSTM was developed.
  • CNN processed the climatic dataset, while RNN handled the cocoa yield dataset for southwest Nigeria.
  • LSTM was employed to mitigate vanishing and exploding gradient issues inherent in CNN-RNN models.

Main Results:

  • The proposed CNN-RNN with LSTM model was benchmarked against other machine learning algorithms.
  • Performance was evaluated using Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE).
  • The CNN-RNN with LSTM model achieved the lowest Mean Absolute Error, indicating high prediction efficiency.

Conclusions:

  • The CNN-RNN with LSTM model offers an efficient and accurate solution for cocoa yield prediction.
  • This deep learning approach overcomes limitations of traditional statistical methods.
  • The findings support improved agricultural planning and decision-making through enhanced prediction accuracy.