Triticeae Genomics and Genetics, 2025, Vol.16, No.6, 245-253 http://cropscipublisher.com/index.php/tgg 251 standard procedure at present. Even the same set of methods may yield different results when the experimental conditions are changed. Such instability to some extent restricts the promotion of related achievements in resistance breeding. Looking ahead, the situation might change. Tools such as artificial intelligence and machine learning are gradually being introduced into the breeding process. In the future, it will no longer be a fantasy to screen massive amounts of data through algorithms, predict resistance performance, or even participate in the design of breeding strategies. New models like federated learning may be able to train accurate models without concentrating data, and they are also more efficient and secure. Once these technologies are truly implemented, they will not only increase the success rate of breeding disease-resistant varieties, but may also fundamentally change the way of breeding. However, this path cannot do without continuous cooperation and technological updates in different fields. If one hopes to no longer "chase after problems" when facing complex diseases but to make early plans, an intelligent, high-throughput and multi-disciplinary integrated breeding system will undoubtedly become the mainstream direction in the future. Acknowledgments We are grateful to Dr. W. Hu for this assistance with the serious reading and helpful discussions during the course of this work. 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 Arruda M., Brown P., Brown-Guedira G., Krill A., Thurber C., Merrill K., Foresman B., and Kolb F., 2016, Genome‐wide association mapping of fusarium head blight resistance in wheat using genotyping-by-sequencing, The Plant Genome, 9(2): 1-14. https://doi.org/10.3835/plantgenome2015.04.0028 Buerstmayr M., Steiner B., and Buerstmayr H., 2020, Breeding for fusarium head blight resistance in wheat—progress and challenges, Plant Breeding, 139: 429-454. https://doi.org/10.1111/pbr.12797 Dai Y., Zhang Y., Li C., Wan K., Chen Y., Nie M., and Zhang H., 2025, Gene localization and functional validation of GmPDH1 in soybean against cyst nematode race 4, Plants, 14(12): 1877. https://doi.org/10.3390/plants14121877 Dong Y., Xia X., Ahmad D., Wang Y., Zhang X., Wu L., Jiang P., Zhang P., Yang X., Li G., and He Y., 2023, Investigating the resistance mechanism of wheat varieties to fusarium head blight using comparative metabolomics, International Journal of Molecular Sciences, 24(4): 3214. https://doi.org/10.3390/ijms24043214 Fernando W., Oghenekaro A., Tucker J., and Badea A., 2020, Building on a foundation: advances in epidemiology resistance breeding and forecasting research for reducing the impact of fusarium head blight in wheat and barley, Canadian Journal of Plant Pathology, 43: 495-526. https://doi.org/10.1080/07060661.2020.1861102 Ghimire B., Mergoum M., Martinez-Espinoza A., Sapkota S., Pradhan S., Babar M., Bai G., Dong Y., and Buck J., 2022, Genetics of fusarium head blight resistance in soft red winter wheat using a genome‐wide association study, The Plant Genome, 15(3): e20222. https://doi.org/10.1002/tpg2.20222 Hafeez A., Arora S., Ghosh S., Gilbert D., Bowden R., and Wulff B., 2021, Creation and judicious application of a wheat resistance gene atlas, Molecular Plant, 14(7): 1053-1070. https://doi.org/10.1016/j.molp.2021.05.014 Hu W., Gao D., Wu H., Liu J., Zhang C., Wang J., Jiang Z., Liu Y., Li D., Zhang Y., and Lu C., 2019, Genome-wide association mapping revealed syntenic loci QFhb-4AL and QFhb-5DL for Fusarium head blight resistance in common wheat (Triticum aestivumL.), BMC Plant Biology, 20(1): 29. https://doi.org/10.1186/s12870-019-2177-0 Huang D.S., Chen R.C., and Li J.Q., 2025, Dissecting complex traits in rice: insights from recent GWAS findings, Plant Gene and Trait, 16(2): 47-55. https://doi.org/10.5376/pgt.2025.16.0006 Jiang P., Wu L., Li C., Hao Y., He Y., Zhang P., Wang H., and Zhang X., 2025, Systematic exploration evaluation and application of significant loci for Fusarium head blight resistance in wheat, Plant Disease, 2025. https://doi.org/10.1094/pdis-10-24-2115-re Jin C., Zhou L., Pu Y., Zhang C., Qi H., and Zhao Y., 2025, Application of deep learning for high-throughput phenotyping of seed: a review, Artif, Intell, Rev., 58: 76. https://doi.org/10.1007/s10462-024-11079-5
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