Discrimination and classification of fresh-cut starfruits ( Averrhoa carambola L.) using automated machine vision system

Abstract

Software for detecting the quality features of golden delicious starfruits of Averrhoa carambola L. genus were developed for automated inspection system using machine vision technology. The features considered were colour and shape. The use of artificial classifiers such as linear discrimination analysis and multi-layer perceptron neural network as a tool to detect starfruit maturities such as unripe, underripe, ripe and overripe in HSI colour space were investigated. The colour spectra of matured and unmatured fruits were characterised using all colour features ranging from hue 10 to hue 74, and, using principal hues generated by Wilks-lambda analysis. Experiments performed on 200 independent starfruit samples revealed that linear discriminant analysis after Wilks-lambda analysis was more precise in classification than direct application of linear discriminant analysis. However, the classification accuracy of multi-layer preceptron remained relatively the same before and after feature reduction. Overall, the average correct classification for linear discriminant analysis and multi-layer preceptron was 95.3% and 90.5% respectively during testing stage. Meanwhile the use of Fourier transform was investigated for shape discrimination. This algorithm produced 100% success rate in detecting starfruits by three shape categories: well-formed, slightly deformed and seriously deformed. Both colour and shape analysis was easily affected by the variation of lighting levels, and this contributed to the major classification error.