AI CoExist – Intelligent Wildlife Detection & Deterrence

AI-enabled Vision Systems
NSF SBIR Phase I–funded technology integrates AI-enabled cameras to detect, classify, and track deer, raccoons, and other wildlife species in real time.

Adaptive Deterrence Engine
Edge-based learning modules analyze behavioral patterns and dynamically activate beamformed acoustic deterrents for species-specific targeting.

Predictive Reinforcement Loop
A reinforcement learning layer continuously optimizes deterrence timing, reducing habituation and improving long-term coexistence outcomes.

Detect–Deter–Adapt Framework
The system anticipates animal re-entry patterns, enabling adaptive and humane response strategies that evolve over time.

Open-Source Coexistence Platform
The project supports an open-source ecosystem for data, models, and APIs—empowering researchers, farmers, and communities to collaborate toward sustainable human–wildlife coexistence.

AI-Driven System for Post-Disaster Agricultural Damage Assessment

Rapid damage detection and mapping

• Combines satellite, UAV, and IoT data to identify flood, drought, or storm impacts.

• AI models segment damaged vs. surviving crops and generate near-real-time maps.

Crop loss quantification

• Uses vegetation indices and AI-based yield models to estimate biomass reduction.

• Translates damage into financial loss to support insurance and relief.

Recovery planning

• Ranks priority zones by severity and recovery potential.

• Provides adaptive recommendations for replanting, soil treatment, or crop switching.

Policy and resilience support

• Delivers standardized reports for governments and insurers.

• Builds predictive models from past disasters to strengthen long-term resilience.