Abstract
The purpose of this paper is to offer a machine vision approach for classifying cocoa beans based on their morphological properties. Using traditional machine learning approaches, the shape and size of cocoa beans were retrieved from photographs. A series of image processing techniques are used to extract the features from the photos. Finally, typical machine learning approaches such as KNN, SVM, Decision Tree, and Random Forest are used to divide the cocoa beans into four groups: large, medium, small, and rejected. A comparison of different methodologies is also carried out. Two optimization strategies, Univariate Selection and Feature Importance, are used to maximize retrieved features prior to training the model. For performance analysis, trained models are evaluated using stratified K-fold cross validations and the mean cross validation score is produced. The Random Forest Classifier has the greatest accuracy score of 0.75, according to the results of the experiments. Keywords: Cocoa beans, Classification, Image processing, Machine Learning, Feature Optimization.
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