Maize Genomics and Genetics 2025, Vol.16, No.3, 139-148 http://cropscipublisher.com/index.php/mgg 139 Feature Review Open Access Integrating Genomic Selection and Machine Learning for Predicting Maize Yield Under Drought Jiayi Wu, Huijuan Xu, Qian Li Modern Agricultural Research Center, Cuixi Academy of Biotechnology, Zhuji, 311800, Zhejiang, China Corresponding author: qian.li@cuixi.org Maize Genomics and Genetics, 2025, Vol.16, No.3 doi: 10.5376/mgg.2025.16.0014 Received: 13 Apr., 2025 Accepted: 24 May, 2025 Published: 16 Jun, 2025 Copyright © 2025 Wu et al., 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. Preferred citation for this article: Wu J.Y., Xu H.J., and Li Q., 2025, Integrating genomic selection and machine learning for predicting maize yield under drought, Maize Genomics and Genetics, 16(3): 139-148 (doi: 10.5376/mgg.2025.16.0014) Abstract Drought is one of the most severe abiotic stresses faced by maize (Zea mays L.) production worldwide, which seriously restricts the stability of crop yield. Traditional breeding methods have limited adaptability in the context of complex climate change, and more efficient prediction methods are urgently needed. This study integrates genomic selection (GS) and machine learning (ML) methods, and uses large-scale genotype, phenotype and environmental data to improve the accuracy of maize yield prediction under drought conditions. This article systematically reviews the latest progress in genomic prediction of drought resistance traits, analyzes typical machine learning algorithms suitable for crop modeling, and proposes a strategy for integrating GS and ML and a hybrid model framework construction method. The feasibility and practicality of this method are verified through actual cases such as the CIMMYT drought-resistant maize project and Chinese maize hybrids. At the same time, the model's portability and robustness in different ecological environments are also evaluated. This study provides a theoretical basis and technical path for AI-driven precision breeding, which has important guiding significance for the cultivation of new maize stress-resistant varieties under drought conditions. Keywords Maize; Genomic selection; Machine learning; Drought stress; Yield 1 Introduction Corn is often encountered with drought when it is planted. This situation is very common and will also cause a reduction in yield, which also affects global food security. Corn itself is afraid of water shortage, so many breeding experts and researchers are studying how to make it more drought-resistant (Amadu et al., 2025). In the past, to predict corn yield, people basically relied on its appearance, that is, whether it grew well, and then combined it with some simple statistical analysis. But this method is not very accurate. Because the trait of drought resistance is too complex, it is not determined by one gene, but by several genes working together. And the relationship between genes and the environment is also difficult to explain clearly. In addition, the drought situation is different from year to year, and it is difficult to predict accurately using the old method, which also affects the speed of breeding new drought-resistant varieties (Shikha et al., 2017; Dias et al., 2018; Fernandes et al., 2024). Now the situation is different. Genomic technology and high-throughput phenotyping analysis are developing rapidly, and scientists can collect more and more detailed data. These new technologies also allow us to use better methods to predict yields. For example, genomic selection (GS) can use genome-wide markers to estimate whether a variety is worth breeding. Machine learning (ML) can process these complex data, build models that adapt to different environments and genotypes, and make more accurate predictions (Saleh et al., 2023). There are many benefits to combining GS and ML. It can not only analyze the complex relationships between genes, but also take into account the impact of the environment. And it can use data from different channels. In this way, we can more accurately predict corn yield performance in drought conditions (Azrai et al., 2024; Wu et al., 2024). This study systematically reviewed the research on combining genomic selection with machine learning to predict maize yield under drought stress, explored the background and challenges of maize drought stress, the shortcomings of traditional prediction models, and the emerging potential of the genomic selection-machine
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