Maize Genomics and Genetics 2025, Vol.16, No.3, 139-148 http://cropscipublisher.com/index.php/mgg 145 Next, research may focus more on how to make AI and machine learning algorithms more stable. People also hope that they can make it easier to explain the results and more convenient to use in different regions. At the same time, there must be unified standards for how to collect data, and it is best to have a better digital platform. In this way, breeding, data, and technical teams can communicate and cooperate more conveniently. There are also some issues that cannot be ignored, such as how to protect data privacy and how to make policies fair so that these new technologies can be promoted more reasonably and responsibly. In the future, as the use of AI in breeding becomes more and more mature, it may become a very useful tool to help us select new crops that are more drought-resistant and adaptable to climate change. In this way, it will also be of great help to ensure food security and promote sustainable agricultural development. Acknowledgments We thank Mr J. Wu from the Institute of Life Science of Jiyang College of Zhejiang A&F University for his reading and revising suggestion. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Abbasi M., Váz P., Silva J., and Martins P., 2025, Machine learning approaches for predicting maize biomass yield: leveraging feature engineering and comprehensive data integration, Sustainability, 17(1): 256. https://doi.org/10.3390/su17010256 Adkinson B., Rosenblatt M., Dadashkarimi J., Tejavibulya L., Jiang R., Noble S., and Scheinost D., 2024, Brain-phenotype predictions of language and executive function can survive across diverse real-world data: dataset shifts in developmental populations, Developmental Cognitive Neuroscience, 70: 101464. https://doi.org/10.1016/j.dcn.2024.101464 Ahmad S., Batool A., and Ali Z., 2024, Spatial predictive analysis of drought duration in relation to climate change using interpolation techniques, Stochastic Environmental Research and Risk Assessment, 39: 639-656. https://doi.org/10.1007/s00477-024-02886-x Amadu M., Beyene Y., Chaikam V., Tongoona P., Danquah E., Ifie B., Burgueño J., Prasanna B., and Gowda M., 2025, Genome-wide association mapping and genomic prediction analyses reveal the genetic architecture of grain yield and agronomic traits under drought and optimum conditions in maize, BMC Plant Biology, 25: 135. https://doi.org/10.1186/s12870-025-06135-3 Azrai M., Aqil M., Andayani N., Efendi R., Suarni, Suwardi, Jihad M., Zainuddin B., Salim, Bahtiar, Muliadi A., Yasin M., Hannan M., Rahman, and Syam A., 2024, Optimizing ensembles machine learning, genetic algorithms, and multivariate modeling for enhanced prediction of maize yield and stress tolerance index, Frontiers in Sustainable Food Systems, 8: 1334421. https://doi.org/10.3389/fsufs.2024.1334421 Bhandari A., Bartholomé J., Cao T., Kumari N., Frouin J., Kumar A., and Ahmadi N., 2018, Selection of trait-specific markers and multi-environment models improve genomic predictive ability in rice, PLoS ONE, 14(5): e0208871. https://doi.org/10.1371/journal.pone.0208871 Bm P., 2022, Breeding and deploying multiple stress-tolerant maize varieties in the tropics, Journal of Rice Research, 15: 59-63. https://doi.org/10.58297/ojpn7450 Cabitza F., Campagner A., Soares F., Guadiana-Romualdo L., Challa F., Sulejmani A., Seghezzi M., and Carobene A., 2021, The importance of being external. methodological insights for the external validation of machine learning models in medicine, Computer Methods and Programs in Biomedicine, 208: 106288. https://doi.org/10.1016/j.cmpb.2021.106288 Chen Q., Ying Q.H., Lei K.Z., Zhang J.M., and Liu H.Z., 2024, The integration of genetic markers in maize breeding programs, Bioscience Methods, 15(5): 226-236. https://doi.org/10.5376/bm.2024.15.0023 Dhaliwal J., Panday D., Saha D., Lee J., Jagadamma S., Schaeffer S., and Mengistu A., 2022, Predicting and interpreting cotton yield and its determinants under long-term conservation management practices using machine learning, Computers and Electronics in Agriculture, 199: 107107. https://doi.org/10.1016/j.compag.2022.107107 Dias K., Gezan S., Guimarães C., Nazarian A., Da Costa E Silva L., Parentoni S., De Oliveira Guimarães P., De Oliveira Anoni C., Pádua J., De Oliveira Pinto M., Noda R., Ribeiro C., De Magalhães J., Garcia A., De Souza J., Guimarães L., and Pastina M., 2018, Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials, Heredity, 121: 24-37. https://doi.org/10.1038/s41437-018-0053-6
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