Maize Genomics and Genetics 2025, Vol.16, No.3, 139-148 http://cropscipublisher.com/index.php/mgg 144 Figure 2 Phenotypic contrast of maize hybrids under managed drought stress (a), managed heat stress (b), and managed waterlogging stress (c) screening (Adopted from Prasanna et al., 2021) 7 Concluding Remarks Combining genotyping (GS) and machine learning (ML), using high-throughput gene and trait data, plus environmental information, can more accurately predict complex traits such as yield and drought resistance. Some commonly used machine learning methods, such as random forests, support vector machines, and neural networks, can help us analyze complex relationships between genes and the environment, especially those that are not easily discovered by traditional statistical methods. The study also found that the combined framework built by these methods can effectively improve the prediction accuracy of corn and other crops, and is also helpful for judging stress resistance. In this way, breeding work can be done faster and more accurately. Integrating multiple omics data and adding some carefully selected key features can further improve the prediction effect. GS plus ML is a more flexible approach, especially for dealing with climate change issues such as drought. It can help shorten breeding time and find climate-suitable genotypes more quickly, so that new high-yield and stress-resistant varieties can be cultivated more quickly. Nowadays, many artificial intelligence tools, high-throughput trait analysis technologies, and automated data processing methods have become more and more common. These technologies are gradually driving agriculture to become smarter, and more and more data is used. In this way, precision breeding is not only simpler, but also easier to promote to large-scale planting. However, these new technologies are not without problems. For example, how to combine different data, whether there are enough computing resources, and how to ensure that small farmers can afford and use them are all things we have to consider.
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