Phenotypic fingerprint: A novel approach for high-throughput phenotypic identification in maize seedling association population

The seedling stage (3rd-leaf stage) is a critical period during which maize transitions from heterotrophy to autotrophy. During this phase, functional leaves gradually expand and become the primary organs for photosynthesis, playing a significant role in early biomass accumulation and final yield formation. However, effectively analyzing and accurately interpreting the massive data generated by phenomics technologies remains a challenge in current research. In this study, using the Planteye F500 phenotyping platform and integrating manual measurements, we systematically acquired 33 phenotypic traits from 306 materials of an association population at the seedling stage, covering multiple dimensions such as biomass, morphological structure, leaf morphology, leaf color parameters, spectral physiological indices, and developmental rate. To address the limitations of traditional analytical methods in handling large-scale phenotypic data and insufficient knowledge mining, this study proposes a dual-level phenotypic analysis strategy: (1) At the population level, a phenotypic atlas was constructed to visually display the distribution patterns of multidimensional traits across different subpopulations and materials of various origins, revealing the differential effects of genetic background and ecological adaptability on early development. (2) At the individual level, an innovative phenotypic fingerprint method was proposed, which converts complex traits into unique 18-digit numerical sequences through Z-score normalization and hierarchical encoding, enabling precise identification of individuals and integrated evaluation across traits. Comparison between the proposed framework and existing analytical methods confirmed its reliability and demonstrated clear advantages in both interpretability and efficiency. In summary, this study not only systematically characterizes the phenotypic variation patterns during the maize seedling stage but also proposes a novel methodological framework that enables both population-level profiling and individual-level identification. This approach offers a powerful tool for large-scale genetic resource evaluation, early breeding selection, and phenotype-genotype association studies, thereby providing substantial data support and methodological advances for future research.