Smart wildlife monitoring: evaluating a camera trap enabled with artificial intelligence

dc.contributor.advisorChairperson, Graduate Committee: Jared T. Beaver; Jeffrey C. Mosley (co-chair)en
dc.contributor.authorKaltenbach, Taylor Louise Gregoryen
dc.date.accessioned2024-11-09T17:44:32Z
dc.date.issued2024en
dc.description.abstractWildlife-livestock conflicts, including depredation, disease transmission, and resource competition, present significant challenges to both the ecological and economic aspects of ranching operations. These conflicts can undermine the sustainability of ranching operations as well as the conservation of wildlife in working landscapes. Leveraging timely and precise data on wildlife activity, distribution, and their interactions with livestock are crucial for enhancing ongoing conflict mitigation efforts and to help sustain wildlife on working landscapes. I evaluated the potential of an artificial intelligence (AI)-enabled camera trap to limit false positive images and provide real-time monitoring of wildlife presence while reducing data overload. In Study 1, I compared the performance of a prototype, edge AI-enabled camera trap (Grizzly Systems) with 2 traditional, non-AI camera traps (Browning and Reconyx) at 8 sites across 3 ranches in south-central Montana, USA, from mid-June through mid-September 2023. I also evaluated the influence of site-specific environmental conditions, including air temperature, wind speed, cloud cover, and vegetation type on camera trap performance. The Grizzly Systems camera trap captured fewer false positive images but exhibited a higher rate of missed detections compared to the Browning and Reconyx camera trap models. Across all 3 camera trap models, the probability of positive detections declined with warmer air temperatures and greater wind speeds. In addition, warmer air temperatures positively influenced missed detections by Reconyx and Grizzly Systems camera trap models, but warmer air temperatures negatively influenced missed detections by Browning camera traps. In Study 2, I compared the performance of a cellular-connected AI-enabled Grizzly Systems camera trap, equipped with an automated image processing and notification reduction workflow, to a traditional, non-AI, cellular-connected Reconyx camera trap at 2 sites in south-central Montana, USA from mid-April to mid-June 2023. The AI-enabled, cellular-connected Grizzly Systems camera trap successfully sent real-time notifications of wildlife presence and transmitted significantly fewer false positive images than the cellular-connected Reconyx camera trap. However, the Grizzly Systems camera trap sent substantially fewer notifications of positive detections than the Reconyx camera trap, which are likely attributed to missed detections by the Grizzly Systems camera trap.en
dc.identifier.urihttps://scholarworks.montana.edu/handle/1/18527
dc.language.isoenen
dc.publisherMontana State University - Bozeman, College of Agricultureen
dc.rights.holderCopyright 2024 by Taylor Louise Gregory Kaltenbachen
dc.subject.lcshWildlife monitoringen
dc.subject.lcshCamerasen
dc.subject.lcshArtificial intelligenceen
dc.subject.lcshEvaluationen
dc.subject.lcshWildlife managementen
dc.titleSmart wildlife monitoring: evaluating a camera trap enabled with artificial intelligenceen
dc.typeThesisen
mus.data.thumbpage61en
thesis.degree.committeemembersMembers, Graduate Committee: Lance McNewen
thesis.degree.departmentAnimal & Range Sciences.en
thesis.degree.genreThesisen
thesis.degree.nameMSen
thesis.format.extentfirstpage1en
thesis.format.extentlastpage80en

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