Deep Learning Applied to the Classification of Cocoa Beans (Theobroma cacao L.) According to Fermentation Quality

Abstract

The Theobroma cacao L. bean fermentation is an important post-harvest process for the development of its properties and aroma. Although cocoa fermentation is complex, farmers use empirical methods to determine its degree of fermentation. One of the traditional techniques used to recognize the quality of fermentation is the “Cut Test”, performed by a person manually. However, this type of techniques could have a computer-based alternative. Therefore, in this study, the use of convolutional neural networks (CNN) based on deep learning was analyzed to determine the degree of fermentation of cocoa beans. For this purpose, a model was developed whose performance was verified in terms of precision and confusion matrix. This model achieved a positive accuracy of 82 % and a confusion matrix with favorable numbers on the diagonal elements. These results show that CNN is a viable option for the classification of cocoa beans based on their fermentation.