A precise count of flower number per inflorescence is essential to characterize the reproductive behavior of a vine. Previous efforts to automatize this process by image-based technologies have failed in the development of a universal system that can be applied to multiple grapevine cultivars, or they have been tested in a set of inflorescences of narrow morphological diversity. Here, we have developed an alternative general method in FIJI for the non-destructive counting of visible flowers in RGB images, considering inflorescences from 45 different grapevine genotypes from three progenies segregating for inflorescence morphology. The algorithm, based on the segmentation of the image into regions of interest (ROIs) according to their color and morphology, provided counting results highly correlated to manual ones (R2 = 0.91). Similar results were obtained when validating this tool in an external dataset of 400 images of four grapevine cultivars. Counting values were used for actual flower number estimation by linear modelling using a subset of 45 images, considering a flower density factor to reduce the adverse effect of the variable number of hidden flowers. Our approach allowed the estimation of flower number with satisfactory results, providing useful information for grapevine breeding and research.