Preliminary study on development of cocoa beans fermentation level measurement based on computer vision and artificial intelligence

Abstract

Abstract The fermentation process is an important indicator of cocoa beans’ quality. The standard method used is the Magra test by splitting the cocoa beans and observing the color of the beans with the naked eye to estimate the degree of fermentation. Although, manual estimation systems require specific expertise, which can lead to inconsistency in predicting cocoa bean fermentation rate. This research aims to develop a classification model of two categories of cocoa, i.e., fermented and unfermented cocoa, using computer vision and a machine learning model. Image analysis has been carried out, and color features have been used to train and compare several classification models. After analyzing the data, it was found out that a model that can quantify the standard and accurate measurement of the degree of fermentation of cocoa beans using artificial neural network models so that it can segment, calculate, and grade classification by using color feature extraction, which is the average value of RGB and L*a*b. The Artificial Neural Network (ANN) Multilayer Perceptron (MLP) has been found to be superior compared to other models achieving training and validation accuracy of 94%.