The appearances of agricultural products are important indices for evaluating the quality of commodities
and the characteristics of different varieties. In general, the appearances are evaluated by experts based
on visual observations. However, the concern regarding this method is that it lacks objectivity, and it is
not quantifiable because it depends greatly on an empirical knowledge. In addition, agricultural products
have multiple appearance features; therefore, several of them need to be analyzed simultaneously for
correct evaluation of the appearance. In this study, we developed a new image analysis system that
can simultaneously evaluate multiple appearance characteristics such as the color, shape and size, of
agricultural products in detail. To evaluate the effectiveness of this system, we conducted quality evaluations
and cultivar identification on the basis of cluster analysis, multidimensional scaling and discriminant
analysis of the appearance characteristics. The results of the cluster analysis revealed that
strawberries could be classified on the basis of their appearance characteristics. Furthermore, we were
able to visualize the small differences in the appearance of the fruit based on multiple characteristics
on a two-dimensional surface by performing multidimensional scaling. The results demonstrate that
our system is effective for qualitative evaluations of the appearance of strawberries. The results of the
discriminant analysis revealed that the accuracy of strawberry cultivar classification using 14 cultivars
was