Wheat and hazelnut inspection with impact acoustics time-frequency patterns
Author
İnce, N. F.
Onaran, İbrahim
Tewfik, A. H.
Kalkan, H.
Pearson, T.
Çetin, A. Enis
Yardimci, Y.
Date
2007-06Source Title
2007 ASABE Annual International Meeting, Technical Papers
Publisher
ASABE
Pages
[1] - [9]
Language
English
Type
Conference PaperItem Usage Stats
103
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53
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Abstract
Kernel damage caused by insects and fungi is one of the most common reason for poor flour quality. Cracked hazelnut shells are prone to infection by cancer producing mold. We propose a new adaptive time-frequency classification procedure for detecting cracked hazelnut shells and damaged wheat kernels using impact acoustic emissions recorded by dropping wheat kernels or hazelnut shells on a steel plate. The proposed algorithm is based on a flexible local discriminant bases (F-LDB) procedure. The F-LDB method combines local cosine packet analysis and a frequency axis clustering approach which supports individual time and frequency band adaptation. Discriminant features are extracted from the adaptively segmented acoustic signal, sorted according to a Fisher class separability criterion, post processed by principal component analysis and fed to linear discriminant. We describe experimental results that establish the superior performance of the proposed approach when compared with prior techniques reported in the literature or used in the field. Our approach achieved classification accuracy in paired separation of undamaged wheat kernels from IDK, Pupae and Scab damaged kernels with 96%, 82% and 94%. For hazelnuts the accuracy was 97.1%.
Keywords
Acoustic measurementAdaptive signal processing
Pattern classification
Time-frequency analysis
Acoustic emissions
Algorithms
Pattern recognition
Principal component analysis
Signal processing
Cracked hazelnut
Kernel damage
Crops