Hyperspectral Imaging-Based Evaluation of Seasonal Growth Characteristics in Turfgrass.

Abstract

Efficient phenotyping is essential for accelerating genetic improvement in turfgrass breeding, where manual measurements are labor-intensive. This study evaluated hyperspectral imaging (HSI) as a high-throughput tool for assessing Zoysia spp. breeding populations consisting of 464 genotypes. HSI data (400-1000 nm) were processed through a user-in-the-loop hybrid segmentation pipeline integrating UMAP dimensionality reduction, DBSCAN clustering, Random Forest classification, and pseudo-RGB refinement. To independently assess vegetation classification performance, 10,000 manually annotated reference points from 50 pseudo-RGB images were compared with the automated module, yielding an overall accuracy of 0.9697, a precision of 0.8830, a recall of 0.9240, a specificity of 0.9779, an F1-score of 0.9030, and Cohen's kappa of 0.8851. A Combined Ranking Score (CRS) integrating five vegetation indices and vegetation pixel count was significantly associated with aerial shoot count ( r = -0.445, p r = -0.207, p < 0.001). The highest-ranked genotype showed a 9370.3-pixel increase in vegetation area between 6 and 16 weeks after transplanting, compared with 1417.7 pixels for the lowest-ranked genotype. Classification performance declined under high-coverage conditions, indicating increased mixed-pixel ambiguity in dense canopies. These results suggest that HSI-based CRS can support rapid, objective, and non-destructive relative ranking of density-related vegetative growth in turfgrass breeding. Because the study was conducted at a single location and season and correlations with manual traits were moderate, the framework is best interpreted as a screening and ranking tool rather than a direct predictive model.

Plant phenotyping relevance

HSI画像と解析パイプラインを用いて、芝草の植生面積・密度関連成長形質を高スループットに推定・検証することが研究の中心であり、育種集団の表現型スクリーニング手法に該当する。

Code and data availability

The paper's hyperspectral imaging data, segmentation pipeline outputs, and phenotype measurements are not publicly deposited; the Data Availability Statement states they are available only upon request from the corresponding author. No author code repository or public dataset URL is provided. The only external URLs (NT

No evidence-backed public reproduction asset is currently recorded.