Supervised Methods Applied to the Construction of a Vision System for the Classification of Cocoa Beans in the Cut-Test
Supervised machine learning methods, also known as classification algorithms, have been widely used in the literature for many classification tasks. In this context, some aspects of these algorithms, as the used attributes used and the form they were built, have a direct impact in the system performance. Therefore, in this paper, we evaluate the application of classification algorithms, along with attribute selection, to propose an improved version of a vision system that performs the classification of cocoa beans. The main aim of this investigation is to improve the performance of a cocoa classification system that aims at helping farmers to classify the different cocoa beans based on images of these beans.
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