Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/54695
Full metadata record
DC FieldValueLanguage
dc.contributor.authorCharlton, Gen
dc.contributor.authorFalzon, Gen
dc.contributor.authorShepley, Aen
dc.contributor.authorFleming, P J Sen
dc.contributor.authorBallard, Gen
dc.contributor.authorMeek, P Den
dc.date.accessioned2023-05-08T23:01:54Z-
dc.date.available2023-05-08T23:01:54Z-
dc.identifier.citationWildlife Research, p. A-Jen
dc.identifier.issn1448-5494en
dc.identifier.issn1035-3712en
dc.identifier.urihttps://hdl.handle.net/1959.11/54695-
dc.description.abstract<p><b>Context:</b> Ground baiting is a strategic method for reducing vertebrate pest populations. Best practice involves maximising bait availability to the target species, although sustaining this availability is resource intensive because baits need to be replaced each time they are taken. This study focused on improving pest population management through the novel baiting technique outlined in this manuscript, although there is potential use across other species and applications (e.g. disease management). </p><p><b>Aims:</b> To develop and test an automated, intelligent, and semi-permanent, multi-bait dispenser that detects target species before distributing baits and provides another bait when a target species revisits the site.</p><p><b> Methods:</b> We designed and field tested the Sentinel Bait Station, which comprises a camera trap with in-built species-recognition capacity, wireless communication and a dispenser with the capacity for five baits. A proof-of-concept prototype was developed and validated via laboratory simulation with images collected by the camera. The prototype was then evaluated in the field under real-world conditions with wild-living canids, using non-toxic baits.</p><p><b> Key results:</b> Field testing achieved 19 automatically offered baits with seven bait removals by canids. The underlying image recognition algorithm yielded an accuracy of 90%, precision of 83%, sensitivity of 68% and a specificity of 96% throughout field testing. The response time of the system, from the point of motion detection (within 6-10 m and the field-of-view of the camera) to a bait being offered to a target species, was 9.81 ± 2.63 s.</p><p><b> Conclusion:</b> The Sentinel Bait Station was able to distinguish target species from non-target species. Consequently, baits were successfully deployed to target species and withheld from non-target species. Therefore, this proof-of-concept device is able to successfully provide baits to successive targets from secure on-board storage, thereby overcoming the need for daily bait replacement.</p><p><b> Implications:</b> The proof-of-concept Sentinel Bait Station design, together with the findings and observations from field trials, confirmed the system can deliver multiple baits and increase the specificity in which baits are presented to the target species using artificial intelligence. With further refinement and operational field trials, this device will provide another tool for practitioners to utilise in pest management programs.</p>en
dc.languageenen
dc.publisherCSIRO Publishingen
dc.relation.ispartofWildlife Researchen
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleThe Sentinel Bait Station: an automated, intelligent design pest animal baiting systemen
dc.typeJournal Articleen
dc.identifier.doi10.1071/WR22183en
dcterms.accessRightsBronzeen
local.contributor.firstnameGen
local.contributor.firstnameGen
local.contributor.firstnameAen
local.contributor.firstnameP J Sen
local.contributor.firstnameGen
local.contributor.firstnameP Den
local.profile.schoolOffice of Faculty of Science, Ag, Business and Lawen
local.profile.schoolSchool of Science and Technologyen
local.profile.schoolSchool of Science and Technologyen
local.profile.schoolSchool of Environmental and Rural Scienceen
local.profile.schoolSchool of Environmental and Rural Scienceen
local.profile.schoolSchool of Environmental and Rural Scienceen
local.profile.emailgcharlto@une.edu.auen
local.profile.emailgfalzon2@une.edu.auen
local.profile.emailasheple2@une.edu.auen
local.profile.emailpflemin7@une.edu.auen
local.profile.emailgballar3@une.edu.auen
local.profile.emailpmeek5@une.edu.auen
local.output.categoryC1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.publisher.placeAustraliaen
local.format.startpageAen
local.format.endpageJen
local.peerreviewedYesen
local.title.subtitlean automated, intelligent design pest animal baiting systemen
local.access.fulltextYesen
local.contributor.lastnameCharltonen
local.contributor.lastnameFalzonen
local.contributor.lastnameShepleyen
local.contributor.lastnameFlemingen
local.contributor.lastnameBallarden
local.contributor.lastnameMeeken
dc.identifier.staffune-id:gcharltoen
dc.identifier.staffune-id:gfalzon2en
dc.identifier.staffune-id:asheple2en
dc.identifier.staffune-id:pflemin7en
dc.identifier.staffune-id:gballar3en
dc.identifier.staffune-id:pmeek5en
local.profile.orcid0000-0002-1989-9357en
local.profile.orcid0000-0001-7511-4967en
local.profile.orcid0000-0002-0287-9720en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:1959.11/54695en
local.date.onlineversion2023-04-17-
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleThe Sentinel Bait Stationen
local.relation.fundingsourcenoteFunding for this research was provided by the Department of Agriculture,Water and the Environment through the Centre for Invasive Species Solutions. Dr Ballard, Dr Fleming and Dr Meek were funded by the NSW Department of Primary Industries, and Dr Falzon and Mr Charlton were funded by the University of New Englanden
local.output.categorydescriptionC1 Refereed Article in a Scholarly Journalen
local.search.authorCharlton, Gen
local.search.authorFalzon, Gen
local.search.authorShepley, Aen
local.search.authorFleming, P J Sen
local.search.authorBallard, Gen
local.search.authorMeek, P Den
local.uneassociationYesen
local.atsiresearchNoen
local.sensitive.culturalNoen
local.year.available2023-
local.subject.for2020460304 Computer visionen
local.subject.for2020400708 Mechatronics hardware design and architectureen
local.subject.for2020410404 Environmental managementen
local.subject.seo2020220403 Artificial intelligenceen
local.subject.seo2020220402 Applied computingen
local.subject.seo2020180602 Control of pests, diseases and exotic species in terrestrial environmentsen
local.profile.affiliationtypeUNE Affiliationen
local.profile.affiliationtypeUNE Affiliationen
local.profile.affiliationtypeUNE Affiliationen
local.profile.affiliationtypeUNE Affiliationen
local.profile.affiliationtypeUNE Affiliationen
local.profile.affiliationtypeUNE Affiliationen
Appears in Collections:Journal Article
School of Environmental and Rural Science
School of Science and Technology
Files in This Item:
2 files
File Description SizeFormat 
Show simple item record

Page view(s)

550
checked on Nov 17, 2024

Download(s)

6
checked on Nov 17, 2024
Google Media

Google ScholarTM

Check

Altmetric


This item is licensed under a Creative Commons License Creative Commons