The content of stone cells is an important factor for pear breeding as a high content indicates severely reduced fruit quality in terms of fruit taste.  Although the frozen-HCl method is currently a common method used to evaluate stone cell content in pears, it is limited in incomplete separation of stone cell and pulp and is time consuming and complicated.  Computer-aided research is a promising strategy in modern scientific research for phenotypic data collection and is increasingly used in studying crops.  Thus far, we lack a quantitative tool that can effectively determine stone cell content in pear fruit.  We developed a program, Pearprocess, based on an imaging protocol using computer vision and image processing algorithms applied to digital images.  Using photos of hand-cut sections of pear fruit stained with phloroglucin-HCl (Wiesner’s reagent), Pearprocess can extract and analyze image-based data to quantify the stone cell-related traits measured in this study: number, size, area and density of stone cell.  We quantified these traits for 395 pear accessions by Pearprocess and revealed large variation in different pear varieties and species.  The number of stone cells varied greatly from value of 138 to 2 866, the density of stone cells ranged from 0.0019 to 0.0632 cm² cm^–2, the distribution of stone cell area ranged from 0.06 to 2.02 cm², and the stone cell size was between 2e-4 and 1e-3 cm².  Moreover, trait data were correlated with fruit taste data.  We found that stone cell density is likely the most important factor affecting the taste of pear fruit.  In summary, Pearprocess is a new cost-effective web-application for semi-automated quantification of two-dimensional phenotypic traits from digital imagery using an easy imaging protocol.  This simpler, feasible and accurate method to evaluate stone cell traits of fruit is a promising new tool for use in evaluating future germplasms for crop breeding programs.