MACHINE LEARNING CLASSIFIER FOR DALIUM GUINEENSE FRUIT USING ITS PHYSICAL PROPERTIES
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- Abstract
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Dalium guineense (DG) is a wild fruit with a brittle epicarp that may be broken accidentally or intentionally while processing during any of the unit operations thereby creating a binomial mixture. Having a binomial mixture of similar items that need to be separated for processing or storage purposes presents a common challenge. This research aims at selecting an appropriate machine learning classifier for the classification of DG fruits. Fifteen measured physical characteristics of randomly selected 200 DG fruits were obtained. Fifty percent of the fruits were deshelled, while the remaining 50% were whole fruits. Different machine learning classifiers were chosen from Decision Tree (DT), Support Vector Machines (SVM), and K-Nearest Neighbor (KNN) classification models, using the classification learner toolkit of MATLAB. The results revealed that Coarse Gaussian SVM and Cosine KNN presented an outstanding classification accuracy of 98.5% compared to other classifiers under investigation. The two classifiers also attained precision, sensitivity, specificity, and F-scores of 99.0%, 98.0%, 99.0%, and 98.5% respectively. The method deployed in this study demonstrates superiority to those reported in some literature. This research recommends the adoption of either the coarse Gaussian SVM or the cosine KNN as the most appropriate classifier for the DG fruits classification.
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