Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/90842
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electronic and Information Engineering | - |
dc.creator | Wang, B | - |
dc.creator | Mao, Y | - |
dc.creator | Ashry, I | - |
dc.creator | AlFehaid, Y | - |
dc.creator | AlShawaf, A | - |
dc.creator | Ng, TK | - |
dc.creator | Yu, C | - |
dc.creator | Ooi, BS | - |
dc.date.accessioned | 2021-09-03T02:34:29Z | - |
dc.date.available | 2021-09-03T02:34:29Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/90842 | - |
dc.language.iso | en | en_US |
dc.publisher | Molecular Diversity Preservation International (MDPI) | en_US |
dc.rights | © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | The following publication Wang, B.; Mao, Y.; Ashry, I.; Al-Fehaid, Y.; Al-Shawaf, A.; Ng, T.K.; Yu, C.; Ooi, B.S. Towards Detecting Red Palm Weevil Using Machine Learning and Fiber Optic Distributed Acoustic Sensing. Sensors 2021, 21, 1592 is available at https://doi.org/10.3390/s21051592 | en_US |
dc.subject | Fiber optic acoustic sensing | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Red palm weevil | en_US |
dc.title | Towards detecting red palm weevil using machine learning and fiber optic distributed acoustic sensing | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 1 | - |
dc.identifier.epage | 14 | - |
dc.identifier.volume | 21 | - |
dc.identifier.issue | 5 | - |
dc.identifier.doi | 10.3390/s21051592 | - |
dcterms.abstract | Red palm weevil (RPW) is a detrimental pest, which has wiped out many palm tree farms worldwide. Early detection of RPW is challenging, especially in large-scale farms. Here, we introduce the combination of machine learning and fiber optic distributed acoustic sensing (DAS) techniques as a solution for the early detection of RPW in vast farms. Within the laboratory environment, we reconstructed the conditions of a farm that includes an infested tree with ∼12 day old weevil larvae and another healthy tree. Meanwhile, some noise sources are introduced, including wind and bird sounds around the trees. After training with the experimental time-and frequency-domain data provided by the fiber optic DAS system, a fully-connected artificial neural network (ANN) and a convolutional neural network (CNN) can efficiently recognize the healthy and infested trees with high classification accuracy values (99.9% by ANN with temporal data and 99.7% by CNN with spectral data, in reasonable noise conditions). This work paves the way for deploying the high efficiency and cost-effective fiber optic DAS to monitor RPW in open-air and large-scale farms containing thousands of trees. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Sensors, Mar. 2021, v. 21, no. 5, 1592, p. 1-14 | - |
dcterms.isPartOf | Sensors | - |
dcterms.issued | 2021-03 | - |
dc.identifier.scopus | 2-s2.0-85101309175 | - |
dc.identifier.pmid | 33668776 | - |
dc.identifier.eissn | 1424-8220 | - |
dc.identifier.artn | 1592 | - |
dc.description.validate | 202109 bcvc | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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sensors-21-01592-v2.pdf | 29.08 MB | Adobe PDF | View/Open |
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