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

Eliopoulos, Panagiotis A.; Potamitis, Ilyas; Kontodimas, D. C.; Givropoulou, Elftheria

doi:10.1093/jee/tov231

Cited by 43 publications

(26 citation statements)

References 52 publications

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“…The characteristics of the larval signals detected in this study suggests that their detectability is similar to that found previously for the close relatives, S. oryzae, R. dominica, and other stored product insect pests Kiobia et al, 2015;Eliopoulos et al, 2015). Consequently, acoustic devices already in use for detection of stored product insects can be readily adapted to the particular environmental conditions and storage structures in sub-Saharan Africa where P. truncatus and S. zeamais are prominent.…”

Section: Discussionsupporting

confidence: 84%

“…Acoustic devices enable non-destructive, automated detection, and monitoring of insect infestations in grain (Fleurat-Lessard, 1988;Weinard, 1998, Gobernado et al, 2005Schwab and Degoul, 2005;Eliopoulos et al, 2015), including pre-emergent stages of postharvest pests Kiobia et al, 2015). These devices can be incorporated in continuous insect pest surveillance during storage and at ports of exit/entry during grain trade.…”

Section: Introductionmentioning

confidence: 99%

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Frequency and time pattern differences in acoustic signals produced by Prostephanus truncatus (Horn) (Coleoptera: Bostrichidae) and Sitophilus zeamais (Motschulsky) (Coleoptera: Curculionidae) in stored maize

Njoroge

Affognon

Mutungi

et al. 2016

Journal of Stored Products Research

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Frequency spectra and timing patterns of brief, 1e10 ms broadband sound impulses produced by movement and feeding activities of Prostephanus truncatus and Sitophilus zeamais last instars and adults in maize were investigated to find spectral and temporal pattern information useful for distinguishing among these species and stages. The impulse spectra were categorized into five different types of frequency patterns (profiles), designated Broadband, HighF, MidF1, MidF2 and LowF to indicate differences in their peak energies and broadness of frequency range. Groups (trains) of three or more closely spaced impulses, termed bursts, were observed to occur frequently in all recordings, as has been reported for sounds produced by other insects. Mean rates of bursts, mean counts of impulses per burst, and mean rates of impulses in bursts were calculated and compared among the two species and stages. The counts of broadband and MidF2 impulses per burst and the rates of broadband and MidF2 impulses in bursts were significantly different for adult than for 4th instar S. zeamais and either stage of P. truncatus. These findings can be useful in developing an acoustic sensor system for automated detection of hidden insects including P. truncatus and S. zeamais in bulk storage warehouses. The findings are discussed in relation to different movement and feeding behavior patterns that have been identified in these important pests.

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Section: Discussionsupporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Frequency and time pattern differences in acoustic signals produced by Prostephanus truncatus (Horn) (Coleoptera: Bostrichidae) and Sitophilus zeamais (Motschulsky) (Coleoptera: Curculionidae) in stored maize

Njoroge

Affognon

Mutungi

et al. 2016

Journal of Stored Products Research

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“…This is just how the proposed approach was implemented. Related works, such as [1,[9][10][11][12][13][15][16][17][18], perform automated data collection for monitoring and corroborate the present work.…”

Section: Initial Considerationssupporting

confidence: 81%

A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns

Escola¹,

Guido²,

Cardoso³

et al. 2020

Application of Expert Systems - Theoretical and Practical Aspects

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One of the main problems in agriculture is crop pest management, which causes financial damage to farmers. This management is traditionally performed with pesticides; however, with a large area of application, it would be more economically viable and more environmentally recommended to know precisely the regions where there is concrete infestation. In coffee farms, cicada makes a distinctive sound when it hatches after years of underground nymph-shaped living. One possibility of contributing to its management would be the development of a device capable of capturing the sound of the adult cicada in order to detect its presence and to quantify crop insects. This device would be spread across the coffee plots to capture sounds within the widest possible area coverage. With monitoring and quantification data, the manager would have more input for decision-making and could adopt the most appropriate management technique based on concrete information on population density separated by crop region. Thus, this chapter presents an algorithm based on wavelets and support vector machines (SVMs), to detect acoustic patterns in plantations, advising on the presence of cicadas.

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“…Current standards of international trade are used as insect-free detection by standard method (ISO 6639-3,2001). Recently, several detection techniques have been developed for insect detection in stored grain such as electromagnetic spectrum-based methods [1][2][3][4][5][6], acoustic-based methods [7], conductance-based methods [8], and probes [9]. However, some of these techniques are time-consuming, expensive and less efficient to detect the dead insects.…”

Section: Introductionmentioning

confidence: 99%

Insect Identification and Counting in Stored Grain: Image Processing Approach and Application Embedded in Smartphones

Zhu

Wang

Líu

et al. 2018

Mobile Information Systems

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Insects can cause a major loss in stored grain, and early identification and monitoring of insects become necessary for applying corrective action. Considering the effectiveness and practicability, an image processing approach and the corresponding application embedded in smartphones are proposed to identify and count the insects. For the insect images acquired by mobile phones, one sliding window-based binarization is adopted to release their nonuniform brightness, and then connected domain-based histogram statistics is presented to identify and count the insects in stored grain. Finally, the experiments are performed on the corresponding application based on Android, and it has shown that the proposed approach can be applicable for different random insect images from mobile phones with the counting accuracy of 95%, which is superior to the traditional approach.

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

Cited by 43 publications

References 52 publications

Frequency and time pattern differences in acoustic signals produced by Prostephanus truncatus (Horn) (Coleoptera: Bostrichidae) and Sitophilus zeamais (Motschulsky) (Coleoptera: Curculionidae) in stored maize

Frequency and time pattern differences in acoustic signals produced by Prostephanus truncatus (Horn) (Coleoptera: Bostrichidae) and Sitophilus zeamais (Motschulsky) (Coleoptera: Curculionidae) in stored maize

A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns

Insect Identification and Counting in Stored Grain: Image Processing Approach and Application Embedded in Smartphones

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