The progress in the research of AE detection method of old house borer larvae (Hylotrupes bajulus L.) in wooden structures

Krajewski, Adam; Bilski, Piotr; Witomski, Piotr; Bobiński, Piotr; Guz, Jakub

doi:10.1016/j.conbuildmat.2020.119387

Cited by 7 publications

(8 citation statements)

References 18 publications

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“…As mentioned earlier, the important aspect is the threshold mass and activity detectable by the modern sensors. Previously the dependency between the larva activity and the surrounding temperature was shown to conform to the theoretical estimations (Krajewski et al 2020). Now, it is important to evaluate the influence of the insect maturity on the generated sounds.…”

Section: Introductionmentioning

confidence: 69%

“…The applied measurement system was described previously (Krajewski et al 2012;Bilski et al 2017;Nowakowska et al 2017;Krajewski et al 2020). Acoustic emission signals were recorded using equipment consisting of a piezoelectric sensor (CCLD model 4507-B-005, Bruel & Kjaer), an external sound card (E-MU Tracker Pre, Creative), and a Lenovo Ideapad laptop with Windows 7 operating system and Adobe Audition software.…”

Section: Methodsmentioning

confidence: 99%

“…Detection of the old house borer larvae is a challenging task. Besides x-ray or computer tomography, the most popular approach is acoustic emission analysis (AEA) (Kerner et al 1980;Plinke 1991;Schofield 2001;Krajewski et al 2012;Creemers 2015;Bilski et al 2017;Nowakowska et al 2017;Krajewski et al 2020). Experiments require relatively simple hardware (either microphones or accelerometers) and often exploit artificial intelligence (AI) methods to classify signals based on previously stored patterns of larva behavior.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of the ability for early detection of newly hatched larvae of Hylotrupes bajulus L. using the acoustic emission method in Scots pine wood

Krajewski,

Bilski,

Witomski

et al. 2024

BioRes

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The acoustic emission analysis method was used to determine the activity of very young Hylotrupes bajulus larvae in their long-term development. In the laboratory conditions the simulated scenario of a fresh, intense infestation inside the construction wood was evaluated. The sounds generated by the insects during their feeding on the wooden samples were processed by the measurement system, which was able to detect and count the larva-originated sound pulses and calculate related energy. So far, no one has examined possibilities of detecting infant larvae of Hylotrupes bajulus L. This work is a continuation of research on the influence of the insect mass on its AE-based detection. The experiments performed with multiple wood samples analyzed through the period of one year have shown dependency between the mass of the larva and the intensity of the sound generated by them. The moment of the earliest possible detection of infestation in the wooden structure was evaluated. In the appropriate conditions (large number of young larvae inside the wood and optimal temperature) the insects reach the mass of tens of mg, which makes them detectable using the AE method.

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Section: Introductionmentioning

confidence: 69%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessment of the ability for early detection of newly hatched larvae of Hylotrupes bajulus L. using the acoustic emission method in Scots pine wood

Krajewski,

Bilski,

Witomski

et al. 2024

BioRes

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“…Acoustic technology provides detection of adult and hidden immature insects feeding in grain [ 59 ], as well as non-destructive detection of adults and immatures in storage bags, museum collections, aquatic habitats [ 286 ], and heritage buildings [ 57 ]. The information it provides is electronically transferable to private and public databases for use by researchers, research data alliances, and plant, animal, and human health protection and regulatory agencies [ 287 ].…”

Section: Discussionmentioning

confidence: 99%

Automated Applications of Acoustics for Stored Product Insect Detection, Monitoring, and Management

Mankin

Hagstrum

Guo

et al. 2021

Insects

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Acoustic technology provides information difficult to obtain about stored insect behavior, physiology, abundance, and distribution. For example, acoustic detection of immature insects feeding hidden within grain is helpful for accurate monitoring because they can be more abundant than adults and be present in samples without adults. Modern engineering and acoustics have been incorporated into decision support systems for stored product insect management, but with somewhat limited use due to device costs and the skills needed to interpret the data collected. However, inexpensive modern tools may facilitate further incorporation of acoustic technology into the mainstream of pest management and precision agriculture. One such system was tested herein to describe Sitophilus oryzae (Coleoptera: Curculionidae) adult and larval movement and feeding in stored grain. Development of improved methods to identify sounds of targeted pest insects, distinguishing them from each other and from background noise, is an active area of current research. The most powerful of the new methods may be machine learning. The methods have different strengths and weaknesses depending on the types of background noise and the signal characteristic of target insect sounds. It is likely that they will facilitate automation of detection and decrease costs of managing stored product insects in the future.

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“…At present, the research of pest acoustic signals mainly focuses on the analysis of the behavior of pests [ 5 , 13 , 14 , 15 ], population density estimation [ 16 ], and acoustic signal recognition of pests [ 17 , 18 , 19 ]. Most of the research data were pure sounds collected in a sound insulation chamber [ 20 ] or indoor environment.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Denoising Using Artificial Intelligence for Wood-Boring Pests Semanotus bifasciatus Larvae Early Monitoring

Liu

Zhang

Jiang

et al. 2022

Sensors

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Acoustic detection technology is a new method for early monitoring of wood-boring pests, and the effective denoising methods are the premise of acoustic detection in forests. This paper used sensors to record Semanotus bifasciatus larval feeding sounds and various environmental noises, and two kinds of sounds were mixed to obtain the noisy feeding sounds with controllable noise intensity. Then, the time domain denoising models and frequency domain denoising models were designed, and the denoising effects were compared using the metrics of a signal-to-noise ratio (SNR), a segment signal-noise ratio (SegSNR), and log spectral distance (LSD). In the experiments, the average SNR increment could achieve 17.53 dB and 11.10 dB using the in the test data using the time domain features and frequency domain features, respectively. The average SegSNR increment achieved 18.59 dB and 12.04 dB, respectively, and the average LSD between pure feeding sounds and denoised feeding sounds were 0.85 dB and 0.84 dB, respectively. The experimental results demonstrated that the denoising models based on artificial intelligence were effective methods for S. bifasciatus larval feeding sounds, and the overall denoising effect was more significant, especially at low SNRs. In view of that, the denoising models using time domain features were more suitable for the forest area and quarantine environment with complex noise types and large noise interference.

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The progress in the research of AE detection method of old house borer larvae (Hylotrupes bajulus L.) in wooden structures

Cited by 7 publications

References 18 publications

Assessment of the ability for early detection of newly hatched larvae of Hylotrupes bajulus L. using the acoustic emission method in Scots pine wood

Assessment of the ability for early detection of newly hatched larvae of Hylotrupes bajulus L. using the acoustic emission method in Scots pine wood

Automated Applications of Acoustics for Stored Product Insect Detection, Monitoring, and Management

Acoustic Denoising Using Artificial Intelligence for Wood-Boring Pests Semanotus bifasciatus Larvae Early Monitoring

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