We present an edge-computing framework for autonomous drones to evaluate and adapt their missions in real time to minimize vigilant behaviors in monitored wildlife. Autonomous drones are increasingly valuable in ecology for capturing high-resolution videos of individual and group behaviors, yet their presence can bias data by provoking vigilance, i.e., heightened awareness triggered by perceived threats. To study this effect, we analyze the KABR (Kenyan Animal Behavior Recognition) dataset, where vigilance is indirectly measured through the frequency and duration of behaviors indicating induced vigilance. We found that vigilant behaviors varied based on environment, flight context, drone hardware, and species of interest, motivating an intelligent edge-to-cloud framework to evaluate the likelihood vigilance in real time.

Wildlife monitoring with drones must balance competing demands: approaching close enough to capture behaviorally-relevant video while avoiding stress responses that compromise animal welfare and data validity. Human operators face a fundamental attentional bottleneck: they cannot simultaneously control drone operations and monitor vigilance states across entire animal groups. By the time elevated vigilance becomes obvious, an adverse flee response by the animals may be unavoidable. To solve this challenge, we present an edge-native, behavior-adaptive drone system for wildlife monitoring. This configurable decision-support system augments operator expertise with automated group-level vigilance monitoring. Our system continuously tracks individual behaviors using YOLOv11m detection and YOLO-Behavior classification, aggregates vigilance states into a real-time group stress metric, and provides graduated alerts (alert vigilance → flee response) with operator-tunable thresholds for context-specific calibration. We derive service-level objectives (SLOs) from video frame rates and behavioral dynamics: to monitor 30fps video streams in real-time, our system must complete detection and classification within 33ms per frame. Our edge-native pipeline achieves 23.8ms total inference on GPU-accelerated hardware, meeting this constraint with a substantial margin. Retrospective analysis of seven wildlife monitoring missions demonstrates detection capability and quantifies the cost of reactive control: manual piloting results in 14 seconds average adverse behavior duration with 71.9% usable frames. Our analysis reveals operators could have received actionable alerts 51s before animals fled in 57% of missions. Simulating 5-second operator intervention yields a projected performance of 82.8% usable frames with 1-second adverse behavior duration, a 93% reduction compared to manual piloting.

Unmanned aerial vehicles, i.e., drones, are well-suited to monitor wildlife behaviors in remote habitats as they can quickly traverse rough terrain inaccessible to humans. However, drone operators must balance (1) potentially aggressive flight tactics to adequately monitor species of interest against (2) animal welfare and the stress that the drone may induce. The navigation strategy must adapt during flight in real-time based on the animals' reactions, while abiding by resource constraints imposed by the environment. We propose an edge-native framework that infers wildlife behaviors during flight and adapts navigation to consider animal welfare. When behavior-adaptive flight (BAF) miscues, the framework engages human-on-the-loop (HoTL) supervision, a light-weight solution to integrate expert feedback. We have validated our approach with high-fidelity digital twin simulations and real-world deployments. Our edge-native system integrates YOLO-v11m object detection and YOLO-Behavior action recognition, operating at 23.8ms inference latency while maintaining strict animal welfare thresholds.

Platforms that run artificial intelligence (AI) pipelines on edge computing resources are transforming the fields of animal ecology and biodiversity, enabling novel wildlife studies in animals' natural habitats. With emerging remote sensing hardware, e.g., camera traps and drones, and sophisticated AI models in situ, edge computing will be more significant in future AI-driven animal ecology (ADAE) studies. However, the study's objectives, the species of interest, its behaviors, range, and habitat, and camera placement affect the demand for edge resources at runtime. If edge resources are under-provisioned, studies can miss opportunities to adapt the settings of camera traps and drones to improve the quality and relevance of captured data. This paper presents salient features of ADAE studies that can be used to model latency, throughput objectives, and provision edge resources. Drawing from studies that span over fifty animal species, four geographic locations, and multiple remote sensing methods, we characterized common patterns in ADAE studies, revealing increasingly complex workflows involving various computer vision tasks with strict service level objectives (SLO). ADAE workflow demands will soon exceed individual edge devices' compute and memory resources, requiring multiple networked edge devices to meet performance demands. We developed a framework to scale traces from prior studies and replay them offline on representative edge platforms, allowing us to capture throughput and latency data across edge configurations. We used the data to calibrate queuing and machine learning models that predict performance on unseen edge configurations, achieving errors as low as 19%.