ArticleBMC plant biology2024
Prediction of Salicornia europaea L. biomass using a computer vision system to distinguish different salt-tolerant populations.
Article in BMC plant biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Cell wall remodeling and polarized light analysis reveal ecotype-specific strategies in Salicornia europaea L. with biotechnological applications.Scientific reports · 2025Article
- Functional Trait Variability ofPlants (Basel, Switzerland) · 2025Article
- Decoding Salinity Tolerance inPlants (Basel, Switzerland) · 2025Article
- Learning fromInternational journal of molecular sciences · 2025Review
- The local environment influences salt tolerance differently in four Salicornia europaea L. inland populations.Scientific reports · 2025Article
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Authors and funding
3 authors.
Funding
Abstract
backgroundSalicornia europaea L. is emerging as a versatile crop halophyte, requiring a low-cost, non-destructive method for salt tolerance classification to aid selective breeding. We propose using a computer vision system (CVS) with multivariate analysis to classify S. europaea based on morphometric and colour traits to predict plant biomass and the salinity in their substrate.
resultsA trial and validation set of 96 and 24 plants from 2 populations confirmed the efficacy. CVS and multivariate analysis evaluated the plants by morphometric traits and CIELab colour variability. Through Pearson analysis, the strongest correlations were between biomass fresh weight (FW) vs. projected area (PA) (0.91) and anatomical cross-section (ACS) vs. shoot diameter (Sd) (0.94). The PA and FW correlation retrieved different equation fits between lower and higher salt-tolerant populations (R
conclusionsThe CVS effectively extracted morphological and colour features from S. europaea cultivated at different salinity levels, enabling classification and plant sorting through image and multivariate analysis. Biomass and salinity substrate were accurately predicted by modelling non-destructive parameters. Enhanced by AI, machine learning and smartphone technology, this method shows great potential in ecology, bio-agriculture, and industry.
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