Bioscience Evidence 2025, Vol.15, No.5, 249-259 http://bioscipublisher.com/index.php/be 256 prediction of complex traits such as yield and stress resistance more reliable. The combination of multi-omics data (such as genes, transcriptomics, metabolomics and other multi-level data) with ensemble learning and deep learning methods has significantly improved the accuracy of yield prediction, with some studies even achieving an increase of over 12%. Meanwhile, new approaches such as high-throughput technology, unmanned aerial vehicle (UAV) remote sensing, and multimodal data fusion have also made the identification and rapid screening of early traits more efficient, thereby accelerating the progress of breeding. In addition, methods like federated learning can achieve data sharing and joint modeling among different institutions without directly exchanging raw data. This not only enhances the performance of the model but also improves the efficiency of resource utilization. Big data has become an important driving force for corn breeding. It has brought about three changes: First, it has shifted breeding decisions from relying on experience to relying on data, achieving more automation and intelligence; Second, through large-scale data integration, the genetic analysis and prediction accuracy of complex traits have been enhanced. Third, it has promoted cross-disciplinary and cross-institutional cooperation and facilitated the development of new concepts such as Intelligent Precision Design Breeding (IPDB). Big data not only accelerates the breeding and promotion of new varieties, but also provides support for addressing climate change and food security. Looking ahead, corn breeding needs to strike a balance among technological innovation, open sharing and sustainable development. Technically, it is necessary to continue developing efficient algorithms, low-cost high-throughput technologies and intelligent decision-making platforms to enhance the efficiency and practicality of big data analysis. In terms of inclusiveness, efforts should be made to promote data sharing and the construction of open platforms, reduce the digital divide, and enable different regions to have fair access to global breeding resources. In terms of sustainability, intelligent breeding should serve green agriculture, cultivate new varieties that are high-yielding, stress-resistant and have high resource utilization rates, and help the global food system transform towards sustainability. Acknowledgments Thank you to the project team for the careful guidance and strong support. 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 Adak A., Kang M., Anderson S., Murray S., Jarquín D., Wong R., and Katzfuss M., 2023, Phenomic data-driven biological prediction of maize through field-based high throughput phenotyping integration with genomic data, Journal of Experimental Botany, 74(17): 5307-5326. https://doi.org/10.1093/jxb/erad216 Andorf C., Beavis W., Hufford M., Smith S., Suza W., Wang K., Woodhouse M., Yu J., and Lübberstedt T., 2019, Technological advances in maize breeding: past, present and future, Theoretical and Applied Genetics, 132: 817-849. https://doi.org/10.1007/s00122-019-03306-3 Barreto C., Dias K., De Sousa I., Azevedo C., Nascimento A., Guimarães L., Guimarães C., Pastina M., and Nascimento M., 2024, Genomic prediction in multi-environment trials in maize using statistical and machine learning methods, Scientific Reports, 14: 1062. https://doi.org/10.1038/s41598-024-51792-3 Beyene Y., Gowda M., Pérez-Rodríguez P., Olsen M., Robbins K., Burgueño J., Prasanna B., and Crossa J., 2021, Application of genomic selection at the early stage of breeding pipeline in tropical maize, Frontiers in Plant Science, 12: 685488. https://doi.org/10.3389/fpls.2021.685488 Bhat S., and Huang N., 2021, Big data and ai revolution in precision agriculture: survey and challenges, IEEE Access, 9: 110209-110222. https://doi.org/10.1109/ACCESS.2021.3102227 Bhuiyan M., Noman I., Aziz M., Rahaman M., Islam M., Manik M., and Das K., 2025, Transformation of plant breeding using data analytics and information technology: innovations, applications, and prospective directions, Frontiers in Bioscience, 17(1): 27936. https://doi.org/10.31083/FBE27936 Cravero A., Pardo S., Sepúlveda S., and Muñoz L., 2022, Challenges to use machine learning in agricultural big data: a systematic literature review, Agronomy, 12(3): 748. https://doi.org/10.3390/agronomy12030748
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