Can edge AI mitigate environmental effects on camera trap performance?

Abstract Abiotic and biotic conditions can affect camera trap performance, and failure to account for environmental factors can bias wildlife research and management inferences modeled from camera trap data. We investigated whether a camera trap enabled with edge artificial intelligence (AI) could mitigate environmental effects on camera trap performance. We compared an edge AI‐enabled prototype with 2 camera trap models commonly used by wildlife managers and researchers in a field experiment in the Greater Yellowstone Ecosystem of south‐central Montana, USA. Camera trap performance was affected by air temperature, wind speed, and time of day. Increased air temperatures and wind speeds decreased the conditional probability of positive detections, and the edge AI‐enabled prototype did not mitigate these effects. The conditional probability of positive detections was <0.15 when air temperatures were ≥30°C or wind speeds were ≥15 km/h. However, when air temperatures were ≥30°C, the conditional probability of false positives was nearly zero for the edge AI‐enabled prototype vs. 0.10 to 1.00 for the camera traps without AI, thereby making image collection and analysis more efficient. Air temperature had no effect on missed detections during crepuscular periods, but during daytime, the conditional probability of missed detections was >0.15 when air temperatures were ≥30°C. During nighttime, the conditional probability of missed detections decreased as air temperature increased, with the conditional probability of missed detections <0.25 when air temperatures were ≥30°C. The edge AI‐enabled prototype did not mitigate time‐of‐day effects on the conditional probability of missed detections, and the edge AI‐enabled prototype was more likely to miss detections than camera traps without AI. The conditional probability of missed detections ranged from 0.20 to 0.80 for the edge AI‐enabled prototype vs. 0.05 to 0.50 for the camera traps without AI. As AI technology advances, edge AI‐enabled camera traps must limit missed detections while continuing to minimize false positives during warm conditions.