Open-shell (turkish) Pistachio Nuts Using Double Tree Un-decimated Wavelet Transform

Due to low consumer acceptance and possibility of having immature kernels, closed-shell pistachio nuts have to be separated from open-shell pistachio nuts. The feasibility of a system using non-contact impact acoustics as a means for classifying closed-shell nuts from open-shell nuts has already been shown with better discrimination performance than a mechanical system. The accuracy of an impact acoustics based system is determined by the signal processing and feature extraction procedures. In this paper a new time-frequency plane feature extraction and classification algorithm was developed to discriminate between open and closed-shell Turkish pistachio nuts. The proposed approach relies on the analysis of impact acoustics signal of a pistachio nut which is emitted by dropping it from a certain height onto a metal plate. Features are extracted by decomposing the acoustic signals into time and frequency components using Dual-Tree un-decimated wavelet transform. The most discriminative features from the dual tree nodes are selected by a greedy search algorithm that combines a structural pruning and classifier feedback. This approach requires no prior knowledge of the relevant time or frequency content of the acoustic signals. The proposed algorithm used a small number of features and achieved a classification accuracy of 91.5% on the validation data set. A previously implemented algorithm, which uses maximum signal amplitude, absolute integration and gradient features, achieved an 82% classification accuracy on the same dataset. The results obtained show that the time-frequency features extracted from impact acoustics can be successfully used in open and closed shell Turkish pistachio classification.