Optimizing and Assessing the Performance of an Algorithm that Cross-correlates Acquired Acoustic Emissions from Internally Feeding Larvae to Count Infested Wheat Kernels in Grain Samples
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- 作者：Weaver ; D. K. ; Shuman ; D. ; Mankin ; R. W.
- 关键词：Quantitation ; algorithm ; hidden larvae ; grain samples ; inspection
- 刊名：Applied Acoustics
- 出版年：1997
- 出版时间：April, 1997
- 年：1997
- 卷：50
- 期：4
- 页码：297-308
- 全文大小：670 K
文摘
An algorithm was developed with optimizable parameters to match sounds from individual insects in grain by cross-correlating signals from an acoustic sensor array. The algorithm was optimized in a series of trials conducted in the sample chamber of an Acoustic Location ‘Fingerprinting’ Insect Detector (ALFID). The sample chamber was filled with uninfested wheat, except for a single kernel, which was infested with an immature rice weevil. This kernel was placed at a known location in the sample chamber. With analysis parameters optimized, the algorithm successfully detected the single insect in 100 % of the trials. The algorithm's capability to count multiple insects was assessed by combining signals in data files collected from single insects into a set that represented sounds from a pair of insects. In these analyses, the algorithm correctly detected the two insects in 100 % of combinations three sensor spacings apart, 100 % of combinations two sensor spacings apart, and 70 % of combinations one sensor spacing apart. Based on these results and the dimensions of the ALFID sampling chamber, the algorithm has a 90 % probability of identifying two randomly located insects producing sounds in a wheat sample.