Classification of the firmness of peaches by sensor fusion

Abstract

The objectives of this research were to compare the performance of each individual nondestructive sensor with the destructive sensor, and to apply sensor fusion technique to explore whether a combination of sensors would give better results than a single sensor for classification of peach firmness. Tests were carried out with four peach varieties namely Royal Glory, Caterina, Tirrenia and Suidring. In this research, the three nondestructive firmness sensors acoustic firmness, low-mass impact and micro-deformation impact were used to measure firmness. A Bayesian classifier was chosen to provide a classification into three categories, namely soft, intermediate and hard. High level fusion technique was performed by using identity declaration provided by each sensor. The data fusion system processed the information of the sensors to output the fused data. The result of the high level fusion was compared with the classification provided by an unsupervised algorithm based on destructive reference measurement. The fusion process of the nondestructive sensors provided some improvements in the firmness classification; the error rate varied from 25% to 19% for individual sensor. Furthermore, the results of fusion process by using three sensors decreased the error rate from 19% to 13%. This research demonstrated that the fused systems provided more complete and complementary information and, thus, were more effective than individual sensors in the firmness classification of peaches.