Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/54695
Title: The Sentinel Bait Station: an automated, intelligent design pest animal baiting system
Contributor(s): Charlton, G  (author); Falzon, G  (author)orcid ; Shepley, A  (author)orcid ; Fleming, P J S  (author); Ballard, G  (author)orcid ; Meek, P D  (author)
Early Online Version: 2023-04-17
Open Access: Yes
DOI: 10.1071/WR22183Open Access Link
Handle Link: https://hdl.handle.net/1959.11/54695
Abstract: 

Context: 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).

Aims: 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.

Methods: 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.

Key results: 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.

Conclusion: 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.

Implications: 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.

Publication Type: Journal Article
Source of Publication: Wildlife Research, p. A-J
Publisher: CSIRO Publishing
Place of Publication: Australia
ISSN: 1448-5494
1035-3712
Fields of Research (FoR) 2020: 460304 Computer vision
400708 Mechatronics hardware design and architecture
410404 Environmental management
Socio-Economic Objective (SEO) 2020: 220403 Artificial intelligence
220402 Applied computing
180602 Control of pests, diseases and exotic species in terrestrial environments
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Environmental and Rural Science
School of Science and Technology

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