Acoustic detection of stem and root borer Neoplocaederus ferrugineus (Coleoptera: Cerambycidae) in cashew

Maruthadurai, R.; Veerakumar, T.; Veershetty, Channabasava; A.N, Sathis Chakaravarthi

doi:10.1016/j.aspen.2022.101968

Cited by 4 publications

(3 citation statements)

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“…The effectiveness of this approach, coupled with its successful application in innovative bioacoustics tasks, has been demonstrated in prior research focused on birdsong classification [17]. Therefore, a future direction is to have our approach-which is very fast and very low in power consumption-do the heavy screening of searching for needles in the hay and direct only a small subset to heavy deep-learning models that will scrutinize these cases especially when one applies low thresholds (that produce false alarms) with the aim to detect early instars and low infestation load [18][19][20].…”

Section: Discussionmentioning

confidence: 99%

Automated Vibroacoustic Monitoring of Trees for Borer Infestation

Potamitis,

Rigakis

2024

Sensors

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In previous research, we presented an apparatus designed for comprehensive and systematic surveillance of trees against borers. This apparatus entailed the insertion of an uncoated waveguide into the tree trunk, enabling the transmission of micro-vibrations generated by moving or digging larvae to a piezoelectric probe. Subsequent recordings were then transmitted at predetermined intervals to a server, where analysis was conducted manually to assess the infestation status of the tree. However, this method is hampered by significant limitations when scaling to monitor thousands of trees across extensive spatial domains. In this study, we address this challenge by integrating signal processing techniques capable of distinguishing vibrations attributable to borers from those originating externally to the tree. Our primary innovation involves quantifying the impulses resulting from the fracturing of wood fibers due to borer activity. The device employs criteria such as impulse duration and a strategy of waiting for periods of relative quietness before commencing the counting of impulses. Additionally, we provide an annotated large-scale database comprising laboratory and field vibrational recordings, which will facilitate further advancements in this research domain.

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Section: Discussionmentioning

confidence: 99%

Automated Vibroacoustic Monitoring of Trees for Borer Infestation

Potamitis,

Rigakis

2024

Sensors

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“…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%

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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