Maize Genomics and Genetics 2025, Vol.16, No.5, 239-250 http://cropscipublisher.com/index.php/mgg 247 8 Future Perspectives and Concluding Remarks In recent years, the pace of the breeding field has indeed changed, so fast that it has caught people off guard. In the past, it would take at least seven or eight years or even longer for a variety to go from breeding to promotion. But what about now? With high-throughput genotyping, multi-omics data and an automated phenotypic platform, the efficiency of many links has been improved by more than a little. But these alone are not enough. It is not uncommon for genetic effects to be handled. The key point is that models are now daring to deal with things that were previously difficult to handle, such as non-additive genetic effects, the interaction between genotypes and the environment, and the indescribable interrelationships between traits. Machine learning has indeed come in handy in this regard, especially as model structures have become increasingly complex. However, no matter how powerful the technology is, it is impossible to master it all by oneself. Things like deep learning, pan-genomics, and AI algorithms, which sound very trendy, can only remain at the level of academic papers in the end without platform support and tool matching. Fortunately, in recent years, many integrated platforms and open-source tools have emerged, at least giving us a glimmer of hope-these models may really have the chance to move from the laboratory to the fields. How can breeding respond to climate change? No one can give a universal answer. But at least one thing is clear: the future breeding process needs to be more "climate-smart". That is to say, not only should high yields be pursued, but also adaptability and stress resistance should be taken into consideration. To achieve this, relying solely on traditional methods is far from enough. The integration of GS-ML is precisely part of this new path. It can help breeders identify materials that can truly "withstand" extreme weather more quickly. However, to make it run, it requires far more than one model-cross-regional data integration, environmental variable access, and multi-trait collaborative modeling-all of which are indispensable. And there are also quite a few practical problems. From data sharing and standard setting among institutions, to the construction of digital infrastructure, and then to the connection and collaboration between breeders and data scientists, every step requires people to do it, money to invest, and a willingness to cooperate. Without solving these problems, the value of technology will also be difficult to be fully released. But then again, don't be dazzled by the "coolness" of the technology itself. The integration of GS and ML is not merely about achieving a few more percentage points of accuracy, but rather offers the opportunity to redefine the breeding process itself, especially in the face of extreme conditions like drought. Of course, there are still quite a few problems. The data is still lacking, the interpretability of the model has not kept up, and the threshold for implementation is also high. These are all realities blocking the way and cannot be resolved in a year or two. But if we can truly make some breakthroughs in these difficult areas, especially by facilitating cross-disciplinary cooperation and integrating what each party excels at, those changes that once seemed distant might come in the blink of an eye. The future is hard to predict, but it's not completely unprepared. Against the backdrop of increasingly unstable climate, these technologies might just become one of the key supports for the food system. Acknowledgments I would like to express my heartfelt gratitude to Ms. Xuan for reviewing the draft of this paper and providing suggestions for improvement, which made the structure and content of the paper clearer and more rigorous. I would also like to thank the two anonymous peer reviewers for their professional reviews and insightful comments, which have helped me further enhance the quality and academic rigor of this paper. Conflict of Interest Disclosure The author affirms 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
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