Computational ethology · Controlled-environment monitoring
BirdConduct AI applies artificial intelligence and deep learning to video from controlled environments — aviaries, farms, wildlife recovery centers, and renewable-energy sites — to detect individual birds, track movement patterns, classify behaviors, and flag early-warning signals of health problems, stress, or environmental impact.
Partner with usDeclining bird populations reduce critical ecosystem services — seed dispersal, pollination, pest control, and nutrient cycling — yet most monitoring remains sporadic, subjective, or logistically infeasible at scale.
Bird communities reflect the health of entire ecosystems. Population-level declines signal habitat degradation, chemical contamination, climate stress, and infrastructure collisions long before other taxa register the same damage.
Computer vision and deep learning are transforming avian monitoring — from the CHIRP dataset (CVPR 2026) for individual-level behavioral tracking of wild populations, to YOLOv10-based real-time detection of ground-nesting birds, multi-view flock tracking, and computational ethology pipelines.
Bird-related infrastructure collisions cost billions annually. Precision welfare monitoring in agriculture and conservation can prevent outbreaks, reduce culling, and optimize resource allocation before problems escalate.
In controlled environments with fixed cameras and known variables, AI models can detect subtle individual-level behavioral shifts — lethargy, limping, disrupted pair bonding — that precede visible illness or environmental failure, enabling pre-emptive intervention.
Our methodology integrates computer vision, time-series analytics, and domain-specific ethological knowledge to produce quantifiable behavioral reports from video feeds.
PILLAR 01
Deep-learning models trained on controlled-environment footage detect individual birds and maintain unique identities across camera frames, enabling longitudinal study of each animal's behavior, movement, and social interactions within the monitored population.
PILLAR 02
Pose-aware and appearance-based models classify discrete ethological states — feeding, preening, aggression, perching, lethargy, limping, pair bonding — with high accuracy, generating continuous behavioral time-stamps for downstream analytics.
PILLAR 03
Trajectory reconstruction and spatial analysis quantify transit patterns — flight corridors, flock cohesion, microhabitat preferences, and daily activity budgets — enabling statistical comparison of normal versus anomalous movement at the individual and population levels.
PILLAR 04
Time-series behavioral models detect statistically significant deviations from baseline profiles, producing severity-ranked alerts for health problems, environmental stressors, or welfare concerns — translating raw video into actionable, evidence-based recommendations.
Controlled-environment monitoring is deployable across any setting where fixed cameras can record birds and behavioral change carries operational, ecological, or ethical significance.
Continuous, non-invasive monitoring of flock health and behavior to optimize husbandry practices and reduce mortality.
Objective assessment of rehabilitation progress through quantifiable behavior metrics and individual tracking.
Wind and solar facility monitoring for collision risk, habitat use, and post-construction impact assessment.
Enrichment evaluation, stress monitoring, and ethological baseline development for managed populations.
Farmland bird monitoring, pest-predator dynamics, and sustainable-farming impact studies using automated ethology.
Our pipeline is built on proven deep-learning architectures, adapted for the unique challenges of controlled-environment avian video: predictable lighting, known camera geometry, and dense flock behavior.
Calibrated multi-angle camera rigs provide continuous, occlusion-minimized coverage of monitored enclosures. Cameras are positioned to maximize individual visibility while preserving natural behavior.
Detection (YOLO-family), multi-object tracking (SORT/DeepSORT), pose estimation, and behavior classification models trained on domain-specific annotated datasets from controlled environments.
Statistical models ingest sequential behavioral classifications to compute activity budgets, transition matrices, social network graphs, and trajectory metrics for each individual bird over time.
On-device inference keeps video data local. Only anonymized behavioral reports — never raw footage — are transmitted. No facial recognition; only species-agnostic behavioral descriptors.
Founded in 2026 and based in Torrevieja, Alicante, Spain.
Founder & Lead Researcher
Applying artificial intelligence and computer vision to solve real-world problems in avian behavioral monitoring and ecological assessment. Leads the research agenda and technical development at BirdConduct AI.
Co-founder, Computational Ethology
Behavioral analysis of captive and wild bird populations using automated ethological methods and computer vision.
Co-founder, Machine Learning & Computer Vision
Detection, tracking, and re-identification models for individual-level avian monitoring in complex environments.
Wildlife Ecology & Conservation Advisor
Conservation ecology and population dynamics expertise applied to AI-driven bird monitoring and habitat assessment.
Embedded Systems & Camera Hardware
Design and deployment of robust, low-power camera systems for continuous remote monitoring in field and facility settings.
The founding team is growing — we are actively collaborating with ornithologists and computational ethology researchers. Get in touch if you work in this space.
Collaborate with usWhether you are a research institution, conservation organization, wildlife facility, or renewable-energy operator, we would welcome a conversation about how automated behavioral monitoring can support your mission.
[email protected]Torrevieja, Alicante, Spain