Morphometric analysis of turfgrass using digital three‐dimensional technology and its application in breeding

Advancements in digital three‐dimensional (3D) imaging technology have enabled precise, high‐throughput, and non‐destructive phenotyping of plant morphology. In this study, we developed a digital phenotyping system specifically tailored for zoysiagrass ( Zoysia species), integrating image‐based 3D model reconstruction, machine learning, and computational trait analysis. By employing a structure from motion approach, we reconstructed detailed 3D models of zoysiagrass using four industrial cameras and an automated imaging platform. A machine learning algorithm was applied to accurately isolate plant components from non‐plant elements. From these segmented models, we extracted key morphological traits—height, spread area, color, and volume—providing a comprehensive dataset for breeding applications. As a digitally derived trait, volume offers new potential in characterizing plant architecture and assessing yield‐related traits non‐destructively. Additionally, we developed a small‐scale, low‐cost prototype system using Raspberry Pi and LEGO‐based components, demonstrating the scalability and adaptability of 3D phenotyping systems across various experimental settings and budgets. Although 3D phenotyping under controlled conditions using potted plants is not directly transferable to field‐based evaluation, it provides essential, reproducible data that bridge early‐stage screening and later field validation in breeding programs. These digital morphological measurements are expected to enhance the precision, repeatability, and objectivity of turfgrass evaluation. As 3D technologies continue to evolve and integrate with genomic and environmental data, digital phenotyping will play an increasingly important role in accelerating turfgrass improvement and promoting data‐driven plant breeding.