Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Prediction Intervals

Prediction Intervals

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.

End Point Prediction: Gran Plot

End Point Prediction: Gran Plot

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...

Residuals and Least-Squares Property

Residuals and Least-Squares Property

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...

You might also read

Related Articles

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

Sort by

Same author

Remotely sensed vegetation indices for crop nutrition mapping.

Journal of the science of food and agriculture·2020

See all related articles

Search research articles

Related Experiment Video

Updated: Dec 13, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Yield prediction with machine learning algorithms and satellite images.

Alireza Sharifi¹

¹Department of Surveying Engineering, Faculty of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.

Journal of the Science of Food and Agriculture

|August 6, 2020

Summary

Accurate barley yield prediction is crucial for food security. This study developed a multi-resource data model using machine learning, finding Gaussian Process Regression best estimates yield one month before harvest.

Keywords:

Gaussian process regression barley machine learning remote sensing yield prediction

Related Experiment Videos

Last Updated: Dec 13, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Area of Science:

Agricultural Science
Remote Sensing
Machine Learning

Background:

Barley yield prediction is vital for global food security.
Integrating remote sensing, field, and meteorological data offers a promising approach.
Machine learning models are key for multi-resource data integration in yield estimation.

Purpose of the Study:

To investigate the impact of different time intervals on barley yield prediction accuracy.
To develop and evaluate a multi-resource data-based estimation model for barley yield.
To compare the performance of various machine learning techniques for yield prediction.

Main Methods:

Dividing the barley growth period into three distinct phases for analysis.
Developing a model that integrates field, remote sensing, and meteorological data.
Evaluating four machine learning algorithms, including Gaussian Process Regression.

Main Results:

Gaussian Process Regression demonstrated superior performance among the tested algorithms.
The model achieved a coefficient of determination (r²) of 0.84.
Accurate yield estimation was possible one month prior to harvest, with specific error metrics provided.

Conclusions:

Yield estimation accuracy is influenced by agricultural zones and temporal training data.
The developed model shows significant potential as a tool for barley yield prediction.
Multi-source data combined with machine learning offers a robust approach to agricultural yield forecasting.