Assessment of bruchids density through bioacoustic detection and artificial neural network (ANN) in bulk stored chickpea and green gram

Banga, Km. Sheetal; a, Nachiket Kotwaliwale; Mohapatra, Debabandya; Babu, V. Bhushana; Giri, Saroj Kumar; Bargale, P. C.

doi:10.1016/j.jspr.2020.101667

Cited by 12 publications

(7 citation statements)

References 19 publications

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“…Eliopoulos et al 16,17 used a single feature (pulse number) to predict the population of the most common beetles in wheat. Banga et al 18 input the formant parameters of food legume bruchids' sounds into an ANN and predicted its population in bulk stored chickpea and green gram. However, this approach has a well-known classification problem: a single 'strong' feature does not exist; therefore, many 'weak' features must be used in parallel for acoustic detection and discrimination.…”

Section: Selection Of Target Sound Signals and Feature Variablesmentioning

confidence: 99%

“…Eliopoulos et al 16,17 successfully estimated the population densities of several stored grain beetles in small containers by developing an automatic monitoring system (a piezoelectric sensor and a portable acoustic emission amplifier connected to the computer). Banga et al 18 assessed bruchids Callosobruchus chinensis (Coleoptera: Bruchidae) densities in bulk stored chickpea and green gram using acoustic detection and artificial neural network (ANN). Despite these successful attempts (timber and storage pests), there is still limited research on detecting wood-boring pests (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Acoustic detection of the wood borer, Semanotus bifasciatus, as an early monitoring technology

Jiang

Liu

Ren

et al. 2022

Pest Management Science

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BACKGROUND Semanotus bifasciatus Motschulsky (Coleoptera: Cerambycidae) is one of the most destructive wood‐boring pests of Platycladus trees in East Asia, threatening the protection of antique cypresses and urban ecological safety. Early identification of Semanotus bifasciatus attacks can help forest managers mitigate the infestation before it turns into an outbreak. Acoustic detection technology is a non‐destructive and continuous monitoring method with the potential to early identify and accurately evaluate the wood‐boring damage. However, few studies have focused on the detection timing and corresponding acoustic features. In this study, we employed a manipulated insect infestation experiment to identify time windows in which early instar Semanotus bifasciatus larvae are most actively boring and feeding within logs and to identify acoustic features that distinguish larval sounds from typical background noise. RESULTS The Semanotus bifasciatus larvae produced sounds most frequently between 13:00 and 20:00 while sounds were detectable from the first to the third instar during the larval growth stage, indicating a suitable time window for early detection. The stepwise regression (SR) model was optimal for detecting the larval instar [coefficient of determination (R2) = 0.71, root mean squared error of prediction (RMSEp) = 0.42, and relative percent deviation (RPD) = 3.38] while the best model for predicting larval population size was the partial least squares regression (PLSR) model (R2 = 0.97, RMSEp = 61.96, and RPD = 28.87). CONCLUSION This study developed an acoustic method for identifying the early attack of Semanotus bifasciatus (including detection time window, feature variables and models for larval instar prediction and population size estimation). This technology integrated with internet of things (IoT) framework can be of value in developing an automated monitoring system for forest wood borer, and provide necessary guidance for integrated pest management (IPM). © 2022 Society of Chemical Industry.

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Section: Selection Of Target Sound Signals and Feature Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Acoustic detection of the wood borer, Semanotus bifasciatus, as an early monitoring technology

Jiang

Liu

Ren

et al. 2022

Pest Management Science

View full text Add to dashboard Cite

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“…Eliopoulos et al (2015Eliopoulos et al ( , 2016) chose a single feature of the pulse number to predicted the population of the most common beetles in wheat. Banga et al (2020) input the formant parameters of sounds into an arti cial neural network for modeling to evaluated and predicted the population of food legume bruchids in bulk stored chickpea and green gram.…”

Section: Selection Of Target Sound Signals and Feature Variablesmentioning

confidence: 99%

“…Eliopoulos et al (2015Eliopoulos et al ( , 2016 successfully predicted the population densities of several stored grain beetles in small containers by using an automatic monitoring system (a piezoelectric sensor and a portable acoustic emission ampli er connected to a computer). Banga et al (2020) assessed bruchids (Callosobruchus chinensis and C. maculatus) density through acoustic detection and arti cial neural network (ANN) in bulk stored chickpea and green gram. Despite these successful attempts, there is still limited research on using acoustic technology to detect wood-boring pests (e.g.…”

Section: Introductionmentioning

confidence: 99%

Early Detection of the Wood-boring Insect Semanotus Bifasciatus Using Acoustic Detection Technology

jiang

Liu

Ren

et al. 2021

Preprint

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BACKGROUND: Semanotus bifasciatus Motschulsky (Coleoptera: Cerambycidae) is one of the most destructive wood-boring pests of Platycladus trees in East Asia, threatening the protection of ancient cypress species and urban ecological safety. Acoustic detection technology has the advantages of high sensitivity, single wood diagnosis and anti-interference, which can be useful for early identification of cryptic wood boring damage. However, there has been limited research on detection time window and acoustics features that suitable for early detection of forest wood borers. METHODS: In this study, we carried out a manipulated insect infestation experiment by inoculating S. bifasciatus into fresh logs, and the feeding sound signals of S. bifasciatus larvae were recorded in timeseries. Then, nine feature variables were selected to characterize the sounds of larval feeding activity. The best time window for acoustic detection during a single day and the whole larval growth stage was determined. And the optimal models for predicting larval instar and population were established using the stepwise regression (SR) and partial least squares regression (PLSR) approach.RESULTS: (1) The single pulse duration of S. bifasciatus was less than 15 ms, and the peak frequency was approximately 8 kHz; (2) Within a 24-hour day, the feeding sound signals were strongest during 13:00 and 20:00; (3) The feeding activity of larvae was greatest during the 1st to the 3rd instar, declined from the 4th instar, and was lowest at the 5th instar; (4) Weak correlations were found between larval instar and feature variables, r ranging from 0.3 to 0.6. By contrast, the larval population has a strong linear correlation with all variables (r>0.7). Except for Average pulse duration and Peak frequency, there indicated high or severe multicollinearity among other variables (the variance inflation factor, VIF >10); (5) The SR model was optimal for predicting larval instar; its prediction accuracy was R2 = 0.71, RMSEp = 0.42, and RPD = 3.38. Average entropy, Peak frequency, and Average pulse duration had the largest influence on the model. (6) The optimal model for predicting population was the PLSR model, and its prediction accuracy was R2 = 0.97, RMSEp = 61.96, and RPD = 28.87. Except for Peak Freq, the other eight variables had a great impact on the model. CONCLUSION: This study highlighted the suitable detection time window and acoustic feature variables for early identification of S. bifasciatus larvae, and optimal models for predicting its larval instar and population were provided. This work will promote further improvements in the efficiency and accuracy of acoustic detection technology for practical applications, providing a reference for evaluating the early damage of wood-boring pest.

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“…The ANN model was used to predict and optimize the main quality parameters of corn for ethanol production (Voca et al., 2021 ). The ANN model has been shown to be an effective and reliable forecasting tool in many studies (Azarmdel et al., 2020 ; Banga et al., 2020 ; Huang et al., 2021 ; Kumar et al., 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Fruit quality prediction based on soil mineral element content in peach orchard

Sun

Huang

Chen

et al. 2022

Food Science & Nutrition

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Mineral nutrition of orchard soil is critical for the growth of fruit trees and improvement of fruit quality. In the present study, the effects of soil mineral nutrients on peach fruit quality were studied by using artificial neural network model. The results showed that the four established ANN models had the highest prediction accuracy (R2 = .9735, .9607, .9036, and .9440, respectively). The results of prediction model sensitivity analysis showed that available B, Ca, N, and K in the soil had the greatest influence on the single fruit weight, available Fe, K, B, and Ca in the soil had the greatest effect on fruit soluble solid content, available Ca, N, B, and K in the soil had the greatest influence on the fruit titratable acid content, and available Ca, Fe, N, and Mn in the soil had the greatest effect on fruit edible rate. The response surface methodology analysis determined the optimal range of these mineral elements, which is critical for guiding precision fertilization in peach orchards and improving peach fruit quality.

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Assessment of bruchids density through bioacoustic detection and artificial neural network (ANN) in bulk stored chickpea and green gram

Cited by 12 publications

References 19 publications

Acoustic detection of the wood borer, Semanotus bifasciatus, as an early monitoring technology

Acoustic detection of the wood borer, Semanotus bifasciatus, as an early monitoring technology

Early Detection of the Wood-boring Insect Semanotus Bifasciatus Using Acoustic Detection Technology

Fruit quality prediction based on soil mineral element content in peach orchard

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