Classification of fermented cocoa beans (cut test) using computer vision

Abstract

Fermentation of cocoa beans is a critical step for chocolate manufacturing, since fermentation influences the development of flavour, affecting components such as free amino acids, peptides and sugars. The degree of fermentation is determined by visual inspection of changes in the internal colour and texture of beans, through the cut-test. Although considered standard for evaluation of fermentation in cocoa beans, this method is time consuming and relies on specialized personnel. Therefore, this study aims to classify fermented cocoa beans using computer vision as a fast and accurate method. Imaging and image analysis provides hand-crafted features computed from the beans, that were used as predictors in random decision forests to classify the samples. A total of 1800 beans were classified into four grades of fermentation. Concerning all image features, 0.93 of accuracy was obtained for validation of unbalanced dataset, with precision of 0.85, recall of 0.81. Although the unbalanced dataset represents actual variation of fermentation, the method was tested for a balanced dataset, to investigate the influence of a smaller number of samples per class, obtaining 0.92, 0.92 and 0.90 for accuracy, precision and recall, respectively. The technique can evolve into an industrial application with a proper integration framework, substituting the traditional method to classify fermented cocoa beans.