Improving wheat yield prediction through variable selection using Support Vector Regression, Random Forest, and Extreme Gradient Boosting

Improving wheat yield prediction through variable selection using Support Vector Regression, Random Forest, and Extreme Gradient Boosting

Plant breeding centers, in their relentless pursuit of more productive and resilient wheat varieties, have generated vast data repositories that are fundamental to ensuring global food security. This study uses these data to develop a wheat grain yield (GY) prediction model, using machine learning t...

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Bibliographic Details
Main Authors: Juan Carlos Moreno Sánchez, Héctor Gabriel Acosta Mesa, Adrián Trueba Espinosa, Sergio Ruiz Castilla, Farid García Lamont
Format: Article
Language:English
Published: Elsevier 2025-03-01
Series:Smart Agricultural Technology
Subjects:
Support vector regression
Random forest
Extreme gradient boosting, grain yield, vegetation indices, climate data
Online Access:http://www.sciencedirect.com/science/article/pii/S2772375525000255
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