Feasibility of impact-acoustic emissions for detection of damaged wheat kernels

Authors:
Tom C. Pearson;A. Enis Cetin;Ahmed H. Tewfik;Ron P. Haff
Affiliations:
USDA-ARS, Manhattan, KS, USA;Bilkent University, Ankara, Turkey;University of Minnesota, Minneapolis, MN, USA;USDA-ARS, Albany, CA, USA
Venue:
Digital Signal Processing
Year:
2007

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Abstract

A non-destructive, real time device was developed to detect insect damage, sprout damage, and scab damage in kernels of wheat. Kernels are impacted onto a steel plate and the resulting acoustic signal analyzed to detect damage. The acoustic signal was processed using four different methods: modeling of the signal in the time-domain, computing time-domain signal variances and maximums in short-time windows, analysis of the frequency spectrum magnitudes, and analysis of a derivative spectrum. Features were used as inputs to a stepwise discriminant analysis routine, which selected a small subset of features for accurate classification using a neural network. For a network presented with only insect damaged kernels (IDK) with exit holes and undamaged kernels, 87% of the former and 98% of the latter were correctly classified. It was also possible to distinguish undamaged, IDK, sprout-damaged, and scab-damaged kernels.