Archives of Acoustics, 42, 1, pp. 61–70, 2017

Detection of Wood Boring Insects’ Larvae Based on the Acoustic Signal Analysis and the Artificial Intelligence Algorithm

Warsaw University of Technology

Warsaw University of Technology

Warsaw University of Life Sciences

Warsaw University of Life Sciences

The paper presents an application of signal processing and computational intelligence methods to detect presence of the wood boring insects larvae in the wooden constructions (such as the furniture of buildings). Such insects are one of the main sources of the degradation in such objects, therefore they should be detected as quickly as possible, before inflicting serious damage. The presented work involved the acoustic monitoring for detecting the presence of the larvae inside pieces of wood. An accelerometer was used to record the sound, further analyzed by a computer algorithm extracting features important for artificial-intelligence (AI) based classification employed to detect the old house borer’s (Hylotrupes bajulus L.) activity. The presented task is difficult, as the sounds made by the larvae are of relatively low amplitude and the background noise caused by people, electrical appliances or other sources may significantly degrade the accuracy of detection. The classification of sounds is needed to separate sources of noise which deteriorate the proper larva detection and should be suppressed if possible. The employed
classification was based on features defined in the time domain followed by the support vector machine used as the binary classifier. The results allowed us to assess the effectiveness of the old house borer’s detection by the acoustic analysis enhanced with the AI algorithm.
Keywords: wood boring insects identification; artificial intelligence classification; accelerometer
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Copyright © Polish Academy of Sciences & Institute of Fundamental Technological Research (IPPT PAN).


Bilski P. (2013), Artificial intelligence methods in the diagnostics of analog systems, Oficyna Wydawnicza Politechniki Warszawskiej, Warsaw.

Chehade N.H., Boureau J.G., Vidal C., Zerubia J. (2009), Multi-class SVM for forestry classification, Proceedings of the 16th IEEE International Conference on Image Processing (ICIP), 1673–1676.

Chiappini E., Nicolialdini R. (2011), Morphological and physiological adaptations of wood-boring beetle larvae in timber, Journal of Entomological and Acarological Research, Ser II, 43, 2, 47–59.

Everest F.A. (2000), Master Handbook of Acoustics, McGraw-Hill, New York.

Farr I. (2007), Automated Bioacoustic Identification of Statutory Quarantined Insect Pests, PhD thesis, University of York.

Fiala P., Friedl M., Cap M., Konas P., Smira P., Naswettrova A. (2014), Non Destructive Method for Detection Wood-destroying Insects, PIERS Proceedings, Guangzhou, China, 1642–1646.

Fleming M.R., Bhardwaj M.C., Janowiak J.J., Shield J.E., Roy R., Agrawal D.K., Bauer L.S., Miller D.L., Hoover K. (2005), Noncontact ultrasound detection of exotic insects in wood packing materials, Forest Products Journal, 55, 6, 33–37.

Hetzroni A., Soroker V., Cohen Y. (2016), Toward practical acoustic red palm weevil detection, Computers and Electronics in Agriculture, 124, 100–106.

Hoffmann N., Schröder T., Schlüter F., Meinlschmidt P. (2013), Potential of infrared thermography to detect insect stages and defects in young trees, Journal Für Kulturpflanzen, 65, 9, 337–346.

Kerner G., Thiele H., Unger W. (1980), Secure and robust detection of the woodworm larvae [in German: Gesicherte und zestörungsfreie Ortung der Larven holzzest¨orender Insekten im Holz], Holztechnologie 21, 131–137.

Krajewski A., Witomski P. (2005), Videoendoscopy as the method dor the asessing the state of the wooden constructons’ monuments [in Polish: Videoendoskopia jako metoda oceny stanu drewnianych konstrukcji w zabytkach], Ochrona Zabytków, 4, 105–108.

Krajewski A., Witomski P., Bobiński P., Wójcik A., Nowakowska M. (2012), An attempt to detect fully-grown house longhorn beetle larvae in coniferous wood based on electroacoustic signals, Drewno. Prace naukowe. Doniesienia. Komunikaty, 55, 108, 5–15.

Kurek J., Osowski S. (2010), Support vector machine for fault diagnosis of the broken rotor bars of squirrelcage induction motor, Neural Comput & Applic, 19, 557–564.

de Lacerda E.G.M., de Carvalho A.C.P.L.F., Ludermir T.B. (2002), A study of cross-validation and bootstrap as objective functions for genetic algorithms, Proceedings of the VII Brazilian Symposium on Neural Networks, 118–123.

Lemaster R.I., Beall F.C., Lewis V.R. (1997), Detection of termites with acoustic emission, Forest Product Journal, 47, 2, 75–79.

Mankin R.W., Weaver D.K., Grieshop M., Larson B., Morril W.L. (2004), Acoustic system for insect detection in plant stems: comparisons of Cephus cinctus in wheat and Metamasius callizona in bromeliads, Journal of Agricultural and Urban Entomology, 21, 239–248.

Mikołajska A., Walczak M., Kaszowska Z., Urbańczyk-Zawadzka M., Banyś R.P. (2012), X-ray techniques in the investigation of a Gothic sculpture: The risen Christ, Nukleonika, 57, 4, 627–631.

Mitchell T.M. (1997), Machine Learning, McGrawHill, New York.

Osterloh K.R.S., Nusser A. (2014) X-ray and neutron radiological methods to support the conservation of wooden artworks soaked with a polluting impregnant ‘Carbolineum’, Proceedings of the 11th European Conference on Non-Destructive Testing (ECNDT 2014), October 6–10, 2014, Prague, Czech Republic.

Patle A., Chouhan D.S. (2013), SVM kernel functions for classification, International Conference on Advances in Technology and Engineering (ICATE), 1– 9.

Pence R.J., Magasin S.J., Nordberg R.G. (1954), Detecting wood-boring insects electronic device developed as aid in locating insects destructive to timber and wood products, California Agriculture, 5.

Schofield J. (2011), Real-time Acoustic Identification of Invasive Wood-boring Beetles, PhD thesis, University of York.

Shen Y., Liu G., Liu H. (2010), Classification method of power quality disturbances based on RVM, Proceedings of the 8th World Congress on Intelligent Control and Automation (WCICA), 6130–6135.

Stirr S.A., Waddle J.R. (1999), Use of CT in Detection of Internal Damage and Repair and Determination of Authenticity in High-Quality Bowed Stringed Instruments, RadioGraphics, 19, 639–646.

Stusek P., Pohleven F., Capl D. (2000), Detection of wood boring insects by measurement of oxygen consumption, International Biodeterioration and Biodegradation, 40, 293–298.

Wood-boring Beetles of Structures, Texas A&M AgriLife Extension, 12/11, 1–5.

Zorović M., Cokl A. (2015), Laser vibrometry as a diagnostic tool for detecting wood-boring beetle larvae, Journal of Pest Science, 88, 107–112.

DOI: 10.1515/aoa-2017-0007