{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:48:54Z","timestamp":1775666934051,"version":"3.50.1"},"reference-count":41,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2024,9,26]],"date-time":"2024-09-26T00:00:00Z","timestamp":1727308800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["scijournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pest Management Science"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>BACKGROUND<\/jats:title><jats:p>Forest ecosystems are under constant threat from wood\u2010boring pests such as the Emerald ash borer (EAB), which remain elusive owing to their hidden life cycles within tree trunks. Early detection is vital to mitigate economic and ecological damage. The main current monitoring method is manual detection which is ineffective at early stages of infestation. This study introduces VibroEABNet, a deep learning\u2010based joint recognition network designed to enhance the detection of EAB boring vibration signals, with a novel approach integrating denoising and recognition modules.<\/jats:p><\/jats:sec><jats:sec><jats:title>RESULTS<\/jats:title><jats:p>The proposed VibroEABNet model demonstrated exceptional performance, achieving an average accuracy of 98.98% across multiple signal\u2010to\u2010noise ratios (SNRs) in test datasets and a remarkable 97.5% accuracy in real forest datasets, surpassing traditional models and other deep learning networks evaluated in this study. These findings were supported by rigorous noise resistance analysis and real dataset evaluation, indicating the model's robustness and reliability in practical applications. Furthermore, the model's efficiency was highlighted by its inference time of 26\u2009ms and a compact model size of 8.43\u2009MB, underscoring its suitability for deployment in resource\u2010limited environments.<\/jats:p><\/jats:sec><jats:sec><jats:title>CONCLUSION<\/jats:title><jats:p>The development of VibroEABNet marks a significant advancement in pest detection methodologies, offering a scalable, accurate and efficient solution for early monitoring of wood\u2010boring pests. The integration of a denoising module within the network structure addresses the challenge of environmental noise, one of the primary limitations in acoustic monitoring of pests. Currently, this research is limited to a specific pest. Future work will focus on the applicability of this network to other wood\u2010boring pests. \u00a9 2024 Society of Chemical Industry.<\/jats:p><\/jats:sec>","DOI":"10.1002\/ps.8442","type":"journal-article","created":{"date-parts":[[2024,9,26]],"date-time":"2024-09-26T06:22:55Z","timestamp":1727331775000},"page":"384-401","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Detecting emerald ash borer boring vibrations using an encoder\u2010decoder and improved <scp>DenseNet<\/scp> model"],"prefix":"10.1002","volume":"81","author":[{"given":"Jinliang","family":"Yin","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Beijing Forestry University  Beijing China"},{"name":"Engineering Research Center for Forestry\u2010Oriented Intelligent Information Processing of National Forestry and Grassland Administration  Beijing 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