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基于声发射检测储存小麦堆中的成虫甲虫

Detection of Adult Beetles Inside the Stored Wheat Mass Based on Their Acoustic Emissions.

作者信息

Eliopoulos P A, Potamitis I, Kontodimas D Ch, Givropoulou E G

机构信息

Technological Educational Institute of Thessaly, Department of Agricultural Technologists, Larissa 41110, Greece.

Technological Educational Institute of Crete, Department of Music Technology and Acoustics, Rethymno 74100, Greece.

出版信息

J Econ Entomol. 2015 Dec;108(6):2808-14. doi: 10.1093/jee/tov231. Epub 2015 Aug 4.

Abstract

The efficacy of bioacoustics in detecting the presence of adult beetles inside the grain mass was evaluated in the laboratory. A piezoelectric sensor and a portable acoustic emission amplifier connected with a computer were used. Adults of the most common beetle pests of stored wheat have been detected in varying population densities (0.1, 0.5, 1, and 2 adults per kilogram of wheat). The verification of the presence of the insect individuals was achieved through automated signal parameterization and classification. We tried out two different ways to detect impulses: 1) by applying a Hilbert transform on the audio recording and 2) by subtracting a noise estimation of the recording from the spectral content of the recording, thus allowing the frequency content of possible impulses to emerge. Prediction for infestation was rated falsely negative in 60-74%, 48-60%, 0-28%, and 0-4% of the cases when actual population density was 0.1, 0.5, 1, and 2 adults per kilogram, respectively, irrespective of pest species. No significant differences were recorded in positive predictions among different species in almost all cases. The system was very accurate (72-100%) in detecting 1 or 2 insects per kilogram of hard wheat grain, which is the standard threshold for classifying a grain mass "clean" or "infested." Our findings are discussed on the basis of enhancing the use of bioacoustics in stored-product IPM framework.

摘要

在实验室中评估了生物声学在检测粮堆内成年甲虫存在情况方面的功效。使用了一个压电传感器和一个与计算机相连的便携式声发射放大器。已在不同种群密度(每千克小麦中有0.1、0.5、1和2只成虫)下检测到储存小麦中最常见甲虫害虫的成虫。通过自动信号参数化和分类实现了对昆虫个体存在情况的验证。我们尝试了两种不同的检测脉冲的方法:1)对音频记录应用希尔伯特变换;2)从记录的频谱内容中减去记录的噪声估计值,从而使可能脉冲的频率内容显现出来。当实际种群密度分别为每千克0.1、0.5、1和2只成虫时,无论害虫种类如何,侵染预测在60 - 74%、48 - 60%、0 - 28%和0 - 4%的情况下被评为假阴性。在几乎所有情况下,不同物种之间的阳性预测没有显著差异。该系统在检测每千克硬小麦粒中有1只或2只昆虫时非常准确(72 - 100%),这是将粮堆分类为“清洁”或“受侵染”的标准阈值。我们基于在储粮害虫综合防治框架中加强生物声学的应用来讨论我们的研究结果。

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