Bioscience Evidence 2025, Vol.15, No.5, 249-259 http://bioscipublisher.com/index.php/be 254 Figure 2 Components of modern plant breeding include not only phenotypic data collected from observed field cultivar trials but also genomic (molecular markers), phenomic (images from drones, airplanes, satellites), and enviromic (temperature, sun radiation, precipitation, soil humidity) data (Adopted from Crossa et al., 2024) 6.3 Socioeconomic and ethical considerations The promotion of big data breeding is also influenced by social and ethical aspects. First of all, data collection, storage and analysis all require high costs and long-term infrastructure support. Developing countries and small and medium-sized breeding institutions often have difficulty affording these costs, which can easily lead to the digital divide (Bhat and Huang, 2021; Lassoued et al., 2021). Secondly, issues of data privacy, security and intellectual property rights are prominent. Many institutions are reluctant to share data, which will affect large-scale cooperation and innovation (Lassoued et al., 2021; Xu et al., 2022). In addition, the governance, standards and ethical norms of agricultural big data are still not perfect. Sensitive issues such as farmers' rights and interests, data ownership and algorithm transparency require multi-party cooperation and policy promotion. 7 Future Perspectives 7.1 Next-generation breeding with big data In the future, corn breeding will enter the "intelligent breeding" stage. It will rely on big data, artificial intelligence (AI) and multi-omics ensemble prediction (such as iGEP) to combine the information of genotype, phenotype and environment (G-P-E) (Xu et al., 2022; Zhu et al., 2024; Liu et al., 2025). AI and machine learning will drive the automation of the entire process, making it more efficient and accurate from gene discovery, functional gene mining to complex trait prediction (Farooq et al., 2024; Wu et al., 2024; Zhu et al., 2024) (Figure 3). Intelligent Precision Design Breeding (IPDB) proposes an open, collaborative and shared platform to promote cooperation among biologists, informatics experts, breeders and farmers. Meanwhile, the combination of cross-species prediction, pan-genome and environmental omics will also bring new ideas for the improvement of complex traits and adaptive breeding.
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