Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Multiple Regression01:25

Multiple Regression

3.3K
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...
3.3K
Regression Analysis01:11

Regression Analysis

7.2K
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:
7.2K
Correlation and Regression00:53

Correlation and Regression

3.8K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
3.8K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.1K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.1K
Microsoft Excel: Regression Analysis01:18

Microsoft Excel: Regression Analysis

1.7K
Regression analysis in Microsoft Excel is a powerful statistical method for examining the relationship between a dependent variable and one or more independent variables. It's used extensively in fields such as economics, biology, and business to predict outcomes, understand relationships, and make data-driven decisions. The most common type is linear regression, which attempts to fit a straight line through the data points to model the relationship between variables.
To perform regression...
1.7K
Residual Plots01:07

Residual Plots

4.7K
A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
When the residual values are plotted against the variable x, it is called a residual...
4.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Comparative analysis of actual evapotranspiration values estimated by METRIC model using LOCAL data and EEFlux for an irrigated area in Northern Sinaloa, Mexico.

Heliyon·2024
Same author

Time Series Complexities and Their Relationship to Forecasting Performance.

Entropy (Basel, Switzerland)·2020
Same author

A Predictive Model for Guillain-Barré Syndrome Based on Single Learning Algorithms.

Computational and mathematical methods in medicine·2017
Same author

Multiphase Simulated Annealing Based on Boltzmann and Bose-Einstein Distribution Applied to Protein Folding Problem.

Advances in bioinformatics·2016
Same author

Towards a predictive model for Guillain-Barré syndrome.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2016
Same author

Feature selection for better identification of subtypes of Guillain-Barré syndrome.

Computational and mathematical methods in medicine·2014

Related Experiment Video

Updated: Apr 27, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

2.9K

Attribute selection impact on linear and nonlinear regression models for crop yield prediction.

Alberto Gonzalez-Sanchez1, Juan Frausto-Solis2, Waldo Ojeda-Bustamante1

  • 1IMTA, Boulevard Cuauhnáhuac 8532, Colonia Progreso, 62550 Jiutepec, MOR, Mexico.

Thescientificworldjournal
|July 1, 2014
PubMed
Summary
This summary is machine-generated.

This study compared data-driven models for crop yield prediction, finding artificial neural networks (ANNs) most effective. ANNs consistently identified optimal features, achieving superior accuracy across eight crops for better agricultural management.

More Related Videos

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
09:44

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

13.3K
O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
06:50

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

Published on: November 8, 2019

5.4K

Related Experiment Videos

Last Updated: Apr 27, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

2.9K
Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
09:44

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

13.3K
O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
06:50

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

Published on: November 8, 2019

5.4K

Area of Science:

  • Agricultural Science
  • Data Science
  • Machine Learning

Background:

  • Accurate crop yield estimation is crucial for efficient agricultural practices.
  • Data-driven models are widely used for yield prediction, but model selection and feature attribution require optimization.
  • Previous comparisons often relied on expert assessment or generic dimensionality reduction for feature selection, potentially hindering model performance.

Purpose of the Study:

  • To evaluate and rank common data-driven modeling techniques for crop yield prediction.
  • To implement a comprehensive method for identifying the optimal feature subset for each regression technique.
  • To compare model performance across multiple crop types using standardized accuracy metrics.

Main Methods:

  • Evaluated multiple linear regression, stepwise linear regression, M5' regression trees, and artificial neural networks (ANNs).
  • Employed a systematic approach to determine the best attribute subset for each model.
  • Trained and validated models using real-world yield data from eight different crops in a Mexican irrigation module.

Main Results:

  • Artificial neural networks (ANNs) demonstrated superior consistency in feature subset selection during learning and training.
  • ANNs achieved the lowest average root relative square error (RRSE) at 86.04% and relative mean absolute error (RMAE) at 8.75%.
  • ANNs yielded the highest average correlation factor (R) at 0.63, indicating stronger predictive power.

Conclusions:

  • Artificial neural networks (ANNs) are the most robust and consistent data-driven modeling technique for crop yield prediction among those evaluated.
  • The method of optimizing feature subsets for each model individually is essential for fair and accurate performance comparisons.
  • Findings support the adoption of ANNs for improved yield estimation and agricultural management, particularly in diverse cropping systems.