Seaweed cultivation faces scalability challenges due to labor-intensive biomass monitoring. Here, we demonstrate a low cost RGB imaging system for Ulva spp biomass estimation (0.5-5.0 g L−1  ) in land-based raceways. Using a gener- alized segmentation model, we extracted area and color features as predictors for linear and log-linear regressions. Surface area proved the strongest predictor (R2 = 0.99, p = <.001, RMSE = 0.18 g L−1  ), with log-linear models outper- forming linear regressions (R2 = 0.88, p = <.001, RMSE = 0.50 g L−1  ) in per revolution analysis. Aggregating frame-level data into three-minute revolutions reduced segmentation errors and accounted for random seaweed distribution, enabling accurate biomass density predictions. This accessible, scalable approach offers a practical solution to reduce labor costs and optimize yields in precision aquaculture.