Classification of Fruits Based on Shape and Color using Combined Nearest Mean Classifiers

Abstract

Fruit classification is an important task in many agriculture industry. The fruit classification system can be used to identify the types and prices of fruit. Manual classification of fruit is not efficient for large amount of fruits. The advancement of information technology has made possible fruit classification be done by a machine. This research aims to propose a fruit classification methodology based on shape and color. To reduce the effect of lighting variability a color normalization is carried out prior to feature extraction. The color features used in this research are mean and standard deviation. The shape features are area, perimeter, and compactness. The classification of an unknown fruit is carried out using the nearest mean classifier. The method developed in this research is tested using 12 classes of fruits where each class is represented by a number of samples. The experimental results show that the method proposed in this research provides an accuracy of 95.83% for two samples per class and 100% for three samples per class. Experiment on small training samples has been conducted to evaluate the performance of the proposed combined nearest mean classifiers and results obtained showed that the technique was able to provide good accuracy.