Weichang Wu

Biotechnology Research Center, Cuixi Academy of Biotechnology, Zhuji, 311800, China
Author    Correspondence author
Maize Genomics and Genetics, 2025, Vol. 16, No. 5   
Received: 05 Jul., 2025    Accepted: 22 Aug., 2025    Published: 07 Sep., 2025

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Drought stress severely constrains maize yields, posing a significant challenge to global food security. This study explores the integration of genomic selection (GS) and machine learning (ML) methods to improve the accuracy of maize yield prediction under drought conditions. First, we outline the principles of GS, highlighting its advantages over traditional breeding methods and its growing application in drought-tolerant breeding. Next, we explore the application of various ML algorithms (such as random forests, support vector machines, and deep learning) for crop yield prediction, along with their strengths and limitations in the context of genomics. We then propose strategies for integrating GS with ML, including hybrid modeling frameworks and context-specific optimization, and discuss recent trends and research advances. Particular emphasis is placed on drought-specific modeling approaches that incorporate stress-responsive traits and evaluate their predictive accuracy under water-deficit environments. A case study from sub-Saharan Africa illustrates the practical application of an integrated GS-ML prediction system and its implications for climate-resilient maize breeding. Despite this promising outlook, challenges remain, including data heterogeneity, model interpretability, and implementation barriers. This study summarizes the future prospects of advancing the integration of genomic selection and machine learning (GS-ML) through technological innovation and its potential to support global climate-smart maize breeding.

Genomic selection; Machine learning; Drought tolerance; Maize yield prediction; Climate-smart breeding