Bioscience Evidence 2025, Vol.15, No.5, 249-259 http://bioscipublisher.com/index.php/be 258 Mora-Poblete F., Maldonado C., Henrique L., Uhdre R., Scapim C., and Mangolim C., 2023, Multi-trait and multi-environment genomic prediction for flowering traits in maize: a deep learning approach, Frontiers in Plant Science, 14: 1153040. https://doi.org/10.3389/fpls.2023.1153040 Najafabadi M., Hesami M., and Eskandari M., 2023, Machine learning-assisted approaches in modernized plant breeding programs, Genes, 14(4): 777. https://doi.org/10.3390/genes14040777 Nepolean T., Kaul J., Mukri G., and Mittal S., 2018, Genomics-enabled next-generation breeding approaches for developing system-specific drought tolerant hybrids in maize, Frontiers in Plant Science, 9: 361. https://doi.org/10.3389/fpls.2018.00361 Onsongo G., Fritsche S., Nguyen T., Belemlih A., Thompson J., and Silverstein K., 2022, ITALLIC: A tool for identifying and correcting errors in location based plant breeding data, Comput. Electron. Agric., 197: 106947. https://doi.org/10.1016/j.compag.2022.106947 Prasanna B., Cairns J., Zaidi P., Beyene Y., Makumbi D., Gowda M., Magorokosho C., Zaman-Allah M., Olsen M., Das A., Worku M., Gethi J., Vivek B., Nair S., Rashid Z., Vinayan M., Issa A., Vicente S., Dhliwayo T., and Zhang X., 2021, Beat the stress: breeding for climate resilience in maize for the tropical rainfed environments, TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik, 134: 1729-1752. https://doi.org/10.1007/s00122-021-03773-7 Sarzaeim P., Muñoz-Arriola F., and Jarquín D., 2022, Climate and genetic data enhancement using deep learning analytics to improve maize yield predictability, Journal of Experimental Botany, 73(15): 5336-5354. https://doi.org/10.1093/jxb/erac146 Sen S., Woodhouse M., Portwood J., and Andorf C., 2023, Maize feature store: a centralized resource to manage and analyze curated maize multi-omics features for machine learning applications, Database: the Journal of Biological Databases and Curation, 2023: baad078. https://doi.org/10.1093/database/baad078 Shu M., Fei S., Zhang B., Yang X., Guo Y., Li B., and Ma Y., 2022, Application of UAV multisensor data and ensemble approach for high-throughput estimation of maize phenotyping traits, Plant Phenomics, 2022: 9802585. https://doi.org/10.34133/2022/9802585 Singh R., Prasad A., Muthamilarasan M., Parida S., and Prasad M., 2020, Breeding and biotechnological interventions for trait improvement: status and prospects, Planta, 252: 54. https://doi.org/10.1007/s00425-020-03465-4 Tong H., and Nikoloski Z., 2020, Machine learning approaches for crop improvement: Leveraging phenotypic and genotypic big data, Journal of Plant Physiology, 257: 153354. https://doi.org/10.1016/j.jplph.2020.153354 Wang B., Zhu L., Zhao B., Zhao Y., Xie Y., Zheng Z., Li Y., Sun J., and Wang H., 2019, Development of a haploid-inducer mediated genome editing system for accelerating maize breeding, Molecular Plant, 12(4): 597-602. https://doi.org/10.1016/j.molp.2019.03.006 Wolfert S., Ge L., Verdouw C., and Bogaardt M., 2017, Big data in smart farming – a review, Agricultural Systems, 153: 69-80. https://doi.org/10.1016/J.AGSY.2017.01.023 Woodhouse M., Cannon E., Portwood J., Harper L., Gardiner J., Schaeffer M., and Andorf C., 2021, A pan-genomic approach to genome databases using maize as a model system, BMC Plant Biology, 21: 385. https://doi.org/10.1186/s12870-021-03173-5 Wu C., Luo J., and Xiao Y., 2024, Multi-omics assists genomic prediction of maize yield with machine learning approaches, Molecular Breeding, 44: 1-17. https://doi.org/10.1007/s11032-024-01454-z Wu H., Han R., Zhao L., Liu M., Chen H., Li W., and Li L., 2025, AutoGP: An intelligent breeding platform for enhancing maize genomic selection, Plant Communications, 6(4): 101240. https://doi.org/10.1016/j.xplc.2025.101240 Xu Y., Zhang X., Li H., Zheng H., Zhang J., Olsen M., Varshney R., Prasanna B., and Qian Q., 2022, Smart breeding driven by big data, artificial intelligence and integrated genomic-enviromic prediction, Molecular Plant, 15(11): 1664-1695. https://doi.org/10.1016/j.molp.2022.09.001 Yan J., and Wang X., 2022, Machine learning bridges omics sciences and plant breeding, Trends in Plant Science, 28(2): 199-210. https://doi.org/10.1016/j.tplants.2022.08.018 Yan J., Xu Y., Cheng Q., Jiang S., Wang Q., Xiao Y., Ma C., Yan J., and Wang X., 2021, LightGBM: accelerated genomically designed crop breeding through ensemble learning, Genome Biology, 22: 271. https://doi.org/10.1186/s13059-021-02492-y Zhang Q., Zhao X., Han Y., Yang F., Pan S., Liu Z., Wang K., and Zhao C., 2023, Maize yield prediction using federated random forest, Comput. Electron. Agric., 210: 107930. https://doi.org/10.1016/j.compag.2023.107930 Zhang Z., Wang X., Zhang Y., Zhou K., Yu G., Yang W., Li F., Guan X., Zhang X., Yang Z., Xu C., and Xu Y., 2025, SPDC‐HG: An accelerator of genomic hybrid breeding in maize, Plant Biotechnology Journal, 23: 1847-1861. https://doi.org/10.1111/pbi.70011
RkJQdWJsaXNoZXIy MjQ4ODYzNA==