In our study, we propose a hybrid Convolutional Neural Network with Support Vector Machine (CNN-SVM) and Principal Component Analysis with support vector machine (PCA-SVM) methods for the classification of cocoa beans obtained by the fermentation of beans collected from cocoa pods after harvest. We also use a convolutional neural network (CNN) and support vector machine (SVM) for the classification operation. In the case of the hybrid model, we use a convolutional network as a feature extractor and the SVM is used to perform the classification operation. The use of PCA-SVM allowed for a reduction in image size while maintaining the main features still using the SVM classifier. Radial, linear and polynomial basis function kernels were used with various control parameters for the SVM, and optimizers such as the Stochastic Gradient Descent (SGD) algorithm, Adam, and RMSprop were used for the CNN softmax classifier. The results showed the robustness of the hybrid CNN-SVM model which obtained the best score with a value of 98.32% then the PCA-SVM based model had a score of 97.65% outperforming the standard CNN and SVM classification algorithms. Metrics such as accuracy, recall, F1 score, mean squared error (MSE), and MCC have allowed us to consolidate the results obtained from our different experiments.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.