Machine learning approach for classification of Dalium guineense fruits

Abstract

Having a mixture of similar items that needs to be separated for processing or for storage is a common challenge. Dalium guineense (DG) is a wild fruit with epicarp that could be broken accidentally or intentionally during harvest or in  the course  of processing. This research attempts to develop a model for classification of DG fruits into whole fruits and  deshelled fruits each with fifteen physical characteristics (Length (l), width (w), thickness (t), geometric mean diameter,  arithmetic mean  diameter, specific mean diameter, equivalent mean diameter, surface area, aspect ratio, surface area, sphericity, unit mass,  lw (product of length and width), lt (product of length and thickness) and wt (product of width and thickness)) using a machine learning approach. A 15-3-2 Neural Network (NN) architecture was used to develop the classification model. The deshelled fruits were all correctly classified while 95 of the whole fruits were correctly classified with 5 of the fruits misclassified. The result shows that the classification model was able to achieve an accuracy of 97.5%, sensitivity of 100%, and precision of 95.2%. Increasing the number of processing elements in the hidden processing layer of the NN contributed no positive effect on the performance of the model. This model is therefore suitable for classification purpose, leading to appropriate processing and handling of DG with high accuracy.