A rapid, high-throughput, non-invasive approach for assessing drought tolerance in rose (Rosa spp.) using RGB-derived vegetation indices

Abstract

Drought stress poses a significant threat to rose (Rosa spp.) cultivation, impacting plant vigor, floral quality and marketability. Traditional drought screening methods are often destructive and labor-intensive, limiting their application in large-scale breeding programs. This study presents a non-destructive and high-throughput phenotyping approach for assessing drought responses in rose using RGB-derived vegetation indices (VIs) obtained from multi-angle imaging. Twenty-eight diverse rose genotypes were evaluated under well-watered (WW) and induced drought (ID) conditions using a LemnaTec Scanalyzer 3D platform. A total of 56 indices from side-view (SV) and top-view (TV) images were computed to quantify canopy color, greenness and pigment-related traits. Analysis of variance revealed significant genotypic differences and strong genotype × treatment (G × T) interactions across most indices, demonstrating their sensitivity to drought-induced physiological changes. Multivariate analyses, including Principal Component Analysis (PCA) and Pearson correlation matrix evaluation, were performed to explore trait relationships and identify key traits associated with drought stress. These analyses effectively differentiated greenness-related and stress-responsive traits. In addition, the MGIDI analysis integrated all indices and identified ‘Queen Elizabeth’, ‘Jwala’, Rosa chinensis, ‘Sylvia’ and ‘Rose Sherbet’ as the top-performing drought-tolerant genotypes. Integration of leaf wilting scores validated the reliability of these indices as accurate indicators of drought response, with tolerant genotypes exhibiting lower LWS and higher greenness indices. Overall, the study demonstrates that RGB-based high-throughput phenotyping provides a rapid, efficient and scalable method for drought tolerance assessment in roses, offering a valuable tool for accelerating selection in ornamental breeding programs.

Plant phenotyping relevance

RGB画像から植生指数を抽出する高スループット表現型解析手法の開発・実証が研究の中心であり、バラの干ばつ応答を評価する再利用可能なワークフローを提示している。

Code and data availability

The article describes RGB-derived vegetation index phenotyping of 28 rose genotypes on a LemnaTec Scanalyzer 3D platform, but no public phenotype dataset, image repository, analysis code, or model deposit is mentioned. Supplementary Tables 1 and 2 are referenced without any availability URL or deposit, and no data or代码

No evidence-backed public reproduction asset is currently recorded.