Acoustic detection of the wood borer, <i>Semanotus bifasciatus,</i> as an early monitoring technology

Jiang, Qi; Liu, Yu Jie; Ren, Lili; Sun, Yu; Luo, Youqing

doi:10.1002/ps.7089

Cited by 5 publications

(7 citation statements)

References 27 publications

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“…Moreover, in environments with significant noise levels, short-duration recognition may lead to misjudgments. The second stage involves the collection of pest sounds or vibrational signals, followed by pest recognition through the use of the Internet of Things (IoT) and algorithms [32,33]. Machine learning stands out as a crucial automated recognition method, leveraging large datasets to discern patterns from raw data and subsequently classifying and predicting new input data.…”

Section: Related Workmentioning

confidence: 99%

A Time-Frequency Domain Mixed Attention-Based Approach for Classifying Wood-Boring Insect Feeding Vibration Signals Using a Deep Learning Model

Jiang,

Chen,

Zhang

2024

Insects

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Wood borers, such as the emerald ash borer and holcocerus insularis staudinger, pose a significant threat to forest ecosystems, causing damage to trees and impacting biodiversity. This paper proposes a neural network for detecting and classifying wood borers based on their feeding vibration signals. We utilize piezoelectric ceramic sensors to collect drilling vibration signals and introduce a novel convolutional neural network (CNN) architecture named Residual Mixed Domain Attention Module Network (RMAMNet).The RMAMNet employs both channel-domain attention and time-domain attention mechanisms to enhance the network’s capability to learn meaningful features. The proposed system outperforms established networks, such as ResNet and VGG, achieving a recognition accuracy of 95.34% and an F1 score of 0.95. Our findings demonstrate that RMAMNet significantly improves the accuracy of wood borer classification, indicating its potential for effective pest monitoring and classification tasks. This study provides a new perspective and technical support for the automatic detection, classification, and early warning of wood-boring pests in forestry.

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Section: Related Workmentioning

confidence: 99%

A Time-Frequency Domain Mixed Attention-Based Approach for Classifying Wood-Boring Insect Feeding Vibration Signals Using a Deep Learning Model

Jiang,

Chen,

Zhang

2024

Insects

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“…The short pulse was only 3 ms and it suddenly rose in the front and gradually decayed over time in the tail, similar to that observed in previously published research. 5,7 The spectral analysis is shown as Fig. 4(b).…”

Section: Acoustic Signatures Of Activity Signalsmentioning

confidence: 99%

“…In recent years, acoustic detection technology has been applied gradually to the field of pest monitoring 1,3,4 . Acoustic detection technology can determine whether a forest is infested with pests without causing serious damage to the tree 5–8 . Thus, it has high research significance and application value in early pest monitoring.…”

Section: Introductionmentioning

confidence: 99%

“…1,3,4 Acoustic detection technology can determine whether a forest is infested with pests without causing serious damage to the tree. [5][6][7][8] Thus, it has high research significance and application value in early pest monitoring. Many automated detection methods have been developed based on acoustic detection technology, such as Gaussian mixture model (GMM) 9 and deep neural networks (DNNs).…”

Section: Introductionmentioning

confidence: 99%

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The interpretability of the activity signal detection model for wood‐boring pests Semanotus bifasciatus in the larval stage

Liu

Chen

Zhang

et al. 2023

Pest Management Science

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BackgroundThe acoustic detection model of activity signals based on deep learning could detect wood‐boring pests accurately and reliably. However, the black‐box characteristics of the deep learning model have limited the credibility of the results and hindered its application. Aiming to address the reliability and interpretability of the model, this paper designed an active interpretable model called Dynamic Acoustic Larvae Prototype Network (DalPNet), which used the prototype to assist model decisions and achieve more flexible model explanation through dynamic feature patch computation.ResultsIn the experiments, the average recognition accuracy of the DalPNet on the simple test set and anti‐noise test set for Semanotus bifasciatus larval activity signals reached 99.3% and 98.5%, respectively. The quantitative evaluation of interpretability was measured by the relative area under the curve (RAUC) and the cumulative slope (CS) of the accuracy change curve in this paper. In the experiments, the RAUC and the CS of DalPNet were 0.2923 and −2.0105, respectively. Additionally, according to the visualization results, the explanation results of DalPNet were more accurate in locating the bite pulses of the larvae and could better focus on multiple bite pulses in one signal, which showed better performance compared to the baseline model.ConclusionThe experimental results demonstrated that the proposed DalPNet had better explanation while ensuring recognition accuracy. In view of that, it could improve the trust of forestry custodians in the activity signals detection model and aid in the practical application of the model in the forestry field. © 2023 Society of Chemical Industry.

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“…It also helps to determine whether conservation, public health or agricultural interventions are needed and/or have been effective (Montgomery et al., 2020; Sinka et al., 2021; WHO, 2022). Timing is key; for example, trees often present symptoms after insect infestation levels become substantial, and hence there is a need for timely monitoring that can locate insects during early stages of infestation before widespread outbreaks (Jiang et al., 2022). Moreover, effective monitoring needs to identify and differentiate among groups of interest.…”

Section: Introductionmentioning

confidence: 99%

From buzzes to bytes: A systematic review of automated bioacoustics models used to detect, classify and monitor insects

Kohlberg,

Myers,

Figueroa

2024

Journal of Applied Ecology

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Insects play vital ecological roles; many provide essential ecosystem services while others are economically devastating pests and disease vectors. Concerns over insect population declines and expansion have generated a pressing need to effectively monitor insects across broad spatial and temporal scales. A promising approach is bioacoustics, which uses sound to study ecological communities. Despite recent increases in machine learning technologies, the status of emerging automated bioacoustics methods for monitoring insects is not well known, limiting potential applications. To address this gap, we systematically review the effectiveness of automated bioacoustics models over the past four decades, analysing 176 studies that met our inclusion criteria. We describe their strengths and limitations compared to traditional methods and propose productive avenues forward. We found automated bioacoustics models for 302 insect species distributed across nine Orders. Studies used intentional calls (e.g. grasshopper stridulation), by‐products of flight (e.g. bee wingbeats) and indirectly produced sounds (e.g. grain movement) for identification. Pests were the most common study focus, driven largely by weevils and borers moving in dried food and wood. All disease vector studies focused on mosquitoes. A quarter of the studies compared multiple insect families. Our review illustrates that machine learning, and deep learning in particular, are becoming the gold standard for bioacoustics automated modelling approaches. We identified models that could classify hundreds of insect species with over 90% accuracy. Bioacoustics models can be useful for reducing lethal sampling, monitoring phenological patterns within and across days and working in locations or conditions where traditional methods are less effective (e.g. shady, shrubby or remote areas). However, it is important to note that not all insect taxa emit easily detectable sounds, and that sound pollution may impede effective recordings in some environmental contexts. Synthesis and applications: Automated bioacoustics methods can be a useful tool for monitoring insects and addressing pressing ecological and societal questions. Successful applications include assessing insect biodiversity, distribution and behaviour, as well as evaluating the effectiveness of restoration and pest control efforts. We recommend collaborations among ecologists and machine learning experts to increase model use by researchers and practitioners.

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

Cited by 5 publications

References 27 publications

A Time-Frequency Domain Mixed Attention-Based Approach for Classifying Wood-Boring Insect Feeding Vibration Signals Using a Deep Learning Model

A Time-Frequency Domain Mixed Attention-Based Approach for Classifying Wood-Boring Insect Feeding Vibration Signals Using a Deep Learning Model

The interpretability of the activity signal detection model for wood‐boring pests Semanotus bifasciatus in the larval stage

From buzzes to bytes: A systematic review of automated bioacoustics models used to detect, classify and monitor insects

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