Development of a digital phenotyping system using 3D model reconstruction for zoysiagrass

Abstract

Abstract Digital phenotyping, particularly the use of plant 3D models, is a promising method for high‐throughput plant evaluation. Although many recent studies on the topic have been published, further research is needed to apply it to breeding research and other related fields. In this study, using a 3D model phenotyping system we developed, we reconstructed and analyzed 20 accessions of zoysiagrass (Zoysia spp.), including three species and their hybrid, over a period of 1 year. Artificial neural network with three hidden layers was able to effectively remove nonplant parts while retaining plant parts that were incorrectly removed using the cropping method, offering a robust and flexible approach for post‐processing of 3D models. The system also demonstrated its ability to accurately evaluate a range of traits, including height, area, and color using red green blue (RGB)‐based vegetation indices. The results showed a high correlation between the estimated volume obtained from voxel 3D model and dry weight, enabling its use as a non‐destructive method for measuring plant volume. In addition, we found that the green red normalized difference index from RGB‐based indices was similar to the commonly used normalized difference vegetation index in controlled illumination conditions. These results demonstrate the potential for three‐dimensional model phenotyping to facilitate plant breeding, particularly in the field of turfgrass and feed crops.

Plant phenotyping relevance

3Dモデル再構築、ニューラルネットワークによる後処理、RGB形質推定を中核とする植物フェノタイピングシステムの開発・検証研究である。

Code and data availability

The paper's data availability statement explicitly deposits the authors' analysis code on GitHub (sandysan42/Zoysia3DModel), which reproduces the 3D-model-based phenotyping analysis (height, area, color, volume). No public phenotype dataset or trained model checkpoint is stated; supporting information is generic.