Diversity assessment of morphological and growth characteristics of zoysiagrass ecotypes in Japan using digital phenotyping

Abstract

Abstract Zoysiagrass ( Zoysia genus) is a valuable warm‐season turfgrass species that exhibits significant diversity in growth and morphological traits across different ecotypes. Traditional methods of classifying zoysiagrass ecotypes rely heavily on morphological observations. However, these methods can be labor‐intensive and may not fully capture the phenotypic diversity present within the species. In this study, we evaluated the utility of non‐invasive digital phenotyping to characterize zoysiagrass ecotypes. Using a digital phenotyping system (DPS) allows for precise measurements of plant height, area, 3D volume (representing biomass), and color index. Clustering algorithms were applied to assess diversity and classify zoysiagrass ecotypes. The subsequent results were compared to manual species classification and genetic marker analysis. The cluster results of DPS effectively differentiate between the three Zoysia species and demonstrate a high correspondence between digital phenotyping and traditional morphological methods. The study highlights the advantages of grouping zoysiagrass based on phenotypic traits and growth characteristics rather than solely on morphological observation or genetic markers, particularly in the context of breeding and research, where a broader range of traits provides more opportunities for selection. Future research could integrate this method with genotypic and transcriptomic analyses for a deeper understanding of zoysiagrass diversity.

Plant phenotyping relevance

デジタルフェノタイピングシステムで植物形質を非侵襲的に測定し、クラスタリングによる分類性能を手動分類・遺伝マーカーと比較しており、フェノタイピング手法の適用と技術評価が中心である。

Code and data availability

The article describes digital phenotyping of 18 zoysiagrass ecotypes (3D imaging, K-means clustering) but contains no data availability statement, no public dataset deposit, and no author code/workflow URL. The DPS method is only cited to a prior paper (Pongpiyapaiboon et al., 2023), and the SSR genotypic dataset comes

No evidence-backed public reproduction asset is currently recorded.