Assessing biomass variability in Salicornia europaea L. populations: leveraging computer vision to distinguish salt-tolerance traits

Abstract

Salicornia europaea is a highly promising halophyte species for bio-based applications, particularly in saline agriculture and sustainable biomass production. To assess its phenotypic responses to salinity stress, we implemented a non-destructive computer vision system (CVS) to quantify morphometric and colour traits. By analyzing 96 plants grown under a gradient of salinity treatments, we developed a robust multivariate model that demonstrated a strong correlation between projected area and fresh weight (r = 0.92). Moreover, the model achieved 100% classification accuracy in distinguishing salt tolerance phenotypes. This CVS-based approach offers a scalable, rapid, and reproducible phenotyping tool suitable for breeding programs, trait-based selection, and ecological monitoring in saline environments.