High-throughput phenotyping of Bambara groundnut [ Vigna subterranea (L.) Verdc.] seed morphometrics using videometer

Abstract Background : Seed size, shape, and colour are key determinants of Bambara groundnut (BGN) seed quality, processing efficiency, and market value. However, conventional assessment methods are slow, subjective, and poorly suited to large-scale characterisation. There is a need for standardised, non-destructive, high-throughput phenotyping methods to support breeding, seed quality assessment, and germplasm evaluation for this resilient yet underutilised crop. Methods : Videometer multispectral imaging (MSI) was used to generate reproducible seed morphometric descriptors for 106 BGN accessions. Twenty seed traits were quantified, including size (area, length, width, perimeter, and volume), shape indices (rectangularity, eccentricity, compactness, form factor, pointness, beta shape, diameter area, width of blob end, and compactness ellipse), and colour metrics (hue, saturation, lightness, and CIELab-A1). Data were analysed using analysis of variance, agglomerative hierarchical clustering, and principal component analysis to assess phenotypic variation. Results : Significant genotypic effects were detected across all measured traits (p < 0.05), indicating substantial phenotypic diversity among the 106 accessions. Seed size and shape traits exhibited wide variation (area ≈ 35–148 mm 2 ; length ≈ 7.3–15.5 mm; and width ≈ 6.5–12.7 mm). Multivariate analyses identified exploratory, dataset-specific phenotypic groupings that broadly reflected relative differences in seed size, shape, and colour among accessions, supporting germplasm characterisation and the identification of contrasting parental material for pre-breeding. Conclusions : MSI-based phenotyping provides a rapid, non-destructive, and high-throughput approach for quantifying seed morphometric variation in BGN. The generated seed trait data provide a standardised basis for germplasm characterisation, the identification of contrasting parental material, and early-stage screening, establishing MSI as a valuable phenotyping foundation for future BGN improvement.