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

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. © 2005 Elsevier Inc. All rights reserved.

• Type: Journal
• Year: 2007
• Where: DSP
• Authors: Tom C. Pearson, A. Enis Çetin, Ahmed H. Tewfik, Ron P. Haff
• Keywords: Acoustic Signal, DSP 2007, DSP 2010, Real Time Device, Time-domain Signal Variances