Schofield, James (2011) Real-time acoustic identiﬁcation of invasive wood-boring beetles. PhD thesis, University of York.
Available under License Creative Commons Attribution-Noncommercial-No Derivative Works 2.0 UK: England & Wales.
Wood-boring beetles are a cause of signiﬁcant economic and environmental cost across the world. A number of species which are not currently found in the United Kingdom are constantly at risk of being accidentally imported due to the volume of global trade in trees and timber. The species which are of particular concern are the Asian Longhorn (Anoplophora glabripennis), Citrus Longhorn (A. chinensis) and Emerald Ash Borer (Agrilus planipennis). The Food and Environment Research Agency’s plant health inspectors currently manually inspect high risk material at the point of import. The development of methods which will enable them to increase the probability of detection of infestation in imported material are therefore highly sought after. This thesis describes research into improving acoustic larvae detection and species identiﬁcation methods, and the development of a real-time system incorporating them.
The detection algorithm is based upon fractal dimension analysis and has been shown to outperform previously used short-time energy based detection. This is the ﬁrst time such a detection method has been applied to the analysis of insect sourced sounds. The species identiﬁcation method combines a time domain feature extraction technique based upon the relational tree representation of discrete waveforms and classiﬁcation using artiﬁcial neural networks. Classiﬁcation between two species, A. glabripennis and H. bajulus, can be performed with 92% accuracy using Multilayer Perceptron and 96.5% accuracy using Linear Vector Quantisation networks. Classiﬁcation between three species can be performed with 88.8% accuracy using LVQ.
A real-time hand-held PC based system incorporating these methods has been developed and supplied to FERA for further testing. This system uses a combination of dual piezo-electric based USB connected sensors and custom written software which can be used to analyse live recordings of larvae in real-time or use previously recorded data.
|Item Type:||Thesis (PhD)|
|Keywords:||acoustic detection, classification, beetle, glabripennis, chinensis, wood-boring|
|Department:||The University of York > Electronics (York)|
|Deposited By:||MR James Schofield|
|Deposited On:||11 Jan 2012 14:47|
|Last Modified:||11 Jan 2012 14:47|
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