Maize Genomics and Genetics 2025, Vol.16, No.5, 239-250 http://cropscipublisher.com/index.php/mgg 241 the differences in drought among different production areas. Some people have combined GS with high-throughput phenotypes and initiated a rapid breeding cycle. The results also proved that in an environment prone to drought, this set of combined measures can indeed bring visible genetic gains (Dias et al., 2018), laying the foundation for the promotion of climate-adapted corn varieties. Figure 1 Effect of drought stress on maize growth and development and the research strategy for the trait improvement. a. An illustration describing the morphological changes that occur in plants in response to drought stress. b. The physiological and cellular responses that occur in maize in response water-deficit conditions and lead to reductions in growth and yield. c Schematic of the research strategy employed in genetic dissection of maize drought resistance for trait enhancement (Adopted from Liu and Qin, 2021) 3 Machine Learning in Maize Crop Yield Prediction 3.1 Types of ML algorithms used The number of machine learning methods currently used to predict corn yields is not an exaggeration. From the earliest linear regression model to the current situation where neural networks and ensemble algorithms are all at work. Traditional methods like Lasso and ridge regression, although "old", still have their place in some simple
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