Exploring phenotypic differences and dynamic associations among lettuce types based on high-throughput phenotyping platform

Abstract

The identification of germplasm resources and analysis of phenotypic traits in lettuce hold significant importance for screening superior varieties and advancing genetic research. To uncover phenotypic differences among lettuce types and their dynamic changes during growth, this study utilized high-throughput phenotyping platform (HTPP) to collect and analyze time-series image data of eight lettuce types. Lettuce phenotypic traits were classified into five major categories: morphology, structure, color, texture, and size, which were further subdivided into 24 subcategories. Using multivariate statistical analysis, the study explored the relationships between phenotypic traits and lettuce types. Combining time-series analysis, the study examined the associations among phenotypic traits, lettuce types, and temporal sequences, revealing the dynamic changes of different lettuce types during their growth processes. The results showed that there were significant differences in phenotypic traits among different lettuce types throughout the growth cycle. These differences reflect the genetic characteristics and phenotypic variation patterns among lettuce types, revealing that genotype guides phenotype formation, while different phenotypes directly influence lettuce growth dynamics. Furthermore, by integrating multidimensional phenotypic traits, we constructed a phenotypic fingerprint for lettuce, providing each lettuce plant with a unique identifier that enables rapid detection of phenotypic differences among lettuce individuals and assists in the selection of elite cultivars. This study provides technical support for precise and rapid identification of lettuce germplasm resources, and can be used as the data basis for genetic research.

Plant phenotyping relevance

レタスの高スループット表現型解析プラットフォームを用いた時系列画像の取得・解析が研究の中心で、多次元形質の抽出とフェノタイプ・フィンガープリント構築を扱っているため。

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