Enhancement of Boring Vibrations Based on Cascaded Dual-Domain Features Extraction for Insect Pest Agrilus planipennis Monitoring

Shi, Haopeng; Chen, Zhibo; Zhang, Haiyan; Li, Juhu; Liu, Xuanxin; Ren, Lili; Luo, Youqing

doi:10.3390/f14050902

Cited by 2 publications

(1 citation statement)

References 69 publications

(82 reference statements)

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“…By leveraging this extensive database derived from a single insect species and mulberry trees (Morus sp. ), one can access impulses and extract embeddings using diverse deep learning models as first suggested in [14] and followed in [15,16]. These embeddings facilitate the adaptation of the models of different deep-learning classifiers to various combinations of wooden substrates and woodboring insect species with minimal sample requirements (i.e., few-shot learning and classification).…”

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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Lightweight Model Design and Compression of CRN for Trunk Borers’ Vibration Signals Enhancement

Zhao,

Li,

Zhang

2023

Forests

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Trunk borers are among the most destructive forest pests. The larvae of some species living and feeding in the trunk, relying solely on the tree’s appearance to judge infestation is challenging. Currently, one of the most effective methods to detect the larvae of some trunk-boring beetles is by analyzing the vibration signals generated by the larvae while they feed inside the tree trunk. However, this method faces a problem: the field environment is filled with various noises that get collected alongside the vibration signals, thus affecting the accuracy of pest detection. To address this issue, vibration signal enhancement is necessary. Moreover, deploying sophisticated technology in the wild is restricted due to limited hardware resources. In this study, a lightweight vibration signal enhancement was developed using EAB (Emerald Ash Borer) and SCM (Small Carpenter Moth) as insect example. Our model combines CRN (Convolutional Recurrent Network) and Transformer. We use a multi-head mechanism instead of RNN (Recurrent Neural Network) for intra-block processing and retain inter-block RNN. Furthermore, we utilize a dynamic pruning algorithm based on sparsity to further compress the model. As a result, our model achieves excellent enhancement with just 0.34M parameters. We significantly improve the accuracy rate by utilizing the vibration signals enhanced by our model for pest detection. Our results demonstrate that our method achieves superior enhancement performance using fewer computing and storage resources, facilitating more effective use of vibration signals for pest detection.

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Enhancement of Boring Vibrations Based on Cascaded Dual-Domain Features Extraction for Insect Pest Agrilus planipennis Monitoring

Cited by 2 publications

References 69 publications

Automated Vibroacoustic Monitoring of Trees for Borer Infestation

Automated Vibroacoustic Monitoring of Trees for Borer Infestation

Lightweight Model Design and Compression of CRN for Trunk Borers’ Vibration Signals Enhancement

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