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

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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.

Section snippets

Tom C. Pearson received his PhD in engineering from University of California, Davis, in 1998. He is currently a lead scientist/engineer at the United States Department of Agriculture—Agricultural Research Service in Manhattan, Kansas. He is also an adjunct faculty at Kansas State University, Department of Biological and Agricultural Engineering. His current research interests are in the development of instrumentation and signal processing methods for rapid inspection and sorting of agricultural

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    • Cited by (0)

    Tom C. Pearson received his PhD in engineering from University of California, Davis, in 1998. He is currently a lead scientist/engineer at the United States Department of Agriculture—Agricultural Research Service in Manhattan, Kansas. He is also an adjunct faculty at Kansas State University, Department of Biological and Agricultural Engineering. His current research interests are in the development of instrumentation and signal processing methods for rapid inspection and sorting of agricultural products. Specific applications include use of imaging, acoustics and near infrared spectroscopy for detecting defects in grains.

    A. Enis Cetin received his PhD in systems engineering in 1987 from the Moore School of Electrical Engineering at University of Pennsylvania in Philadelphia. He is currently a Professor in Electrical and Electronics Engineering at Bilkent University in Ankara, Turkey. His current research interests are in the development of robust voice recognition systems in the presence of noise, motion detection and tracking in video streams for security systems, and applications of digital signal processing for improved inspection accuracy of agricultural products.

    Ahmed H. Tewfik received his PhD in electrical engineering and computer science from the Massachusetts Institute of Technology in 1987. He is currently the E.F. Johnson Professor of Electronic Communications, Department of Electrical Engineering, University of Minnesota in Minneapolis. His current research interests include genomics, datanomic computing, wireless personal area communications, and food inspection.

    Ron P. Haff received his PhD in engineering from University of California, Davis, in 2001. He is currently a lead scientist/engineer at the United States Department of Agriculture—Agricultural Research Service in Albany California. His current research interests are in the development of instrumentation and signal processing methods for rapid inspection and sorting of agricultural products. Specific methods include X-ray imaging and physical methods for detecting defects in fruits, nuts, and grains.

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