SVM optimization using a grid search algorithm to identify robusta coffee bean images based on circularity and eccentricity

Abstract

Coffee variety is one of the main factors affecting the quality and price of coffee, so it is important to recognize coffee varieties. This study aims to optimize the recognition of robusta coffee beans based on circularity and eccentricity image features using a support vector machine (SVM) and Grid search algorithm. The methods used included image acquisition, preprocessing, feature extraction, classification, and evaluation. Circularity and eccentricity are used in the feature extraction process, while the grid search algorithm is used to optimize SVM parameters in the classification process for four different kernels. This study produced the best classification model with the highest accuracy of 94% for the RBF and Polynomial kernels.