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.
Chaitra et al. (Wed,) studied this question.