Implementing trait-based phenotyping for waterlogging stress in crop improvement has become imperative due to the limitations of traditional methods for assessing abiotic stress tolerance. Therefore, there is a crucial need for efficient phenotyping tools and protocols to non-invasively evaluate genotypes for advantageous traits associated with waterlogging tolerance. In this context, the study was carried out to optimize an affordable phenotyping protocol to assess one of such traits, namely waterlogging-induced roots (WIR) in cowpea genotypes. The data generated from optimized protocol for stress imposition, image acquisition, and image analysis demonstrated effectively that WIR image features significantly differentiated cowpea genotypes when they were subjected to waterlogging stress as evidenced by PCA and K-cluster analysis. The study also revealed significant variation among genotypes in terms of WIR architecture based on image features such as total root length (TRL), network area (NA), convex area (CA), volume (Vol) and Median number of roots (MeN) etc. Efficacy of these traits in differentiating the waterlogging tolerant and intolerant genotypes of cowpea could be validated with conventional parameters. A strong positive correlation between conventional and WIR image features indicated that WIR, playing a role in waterlogging tolerance, can be reliably measured noninvasively. Furthermore, the phenotyping protocol developed in this study together with growth parameters could help in identification of waterlogging tolerant genotypes CG121 and CG221 that had enhanced WIR over other genotypes under waterlogging conditions. The affordable phenotyping protocol developed in this study promises to serve as an effective phenotyping tool for assessing waterlogging-induced roots in cowpea and promising genotypes like CG121 and CG221 may serve as donors for waterlogging tolerance.