Idea
Smartphone app and machine learning models for real-time identification of illegal wildlife products aiding law enforcement.
Research Paper
Core Innovation
This paper develops machine learning models that accurately identify products derived from threatened species using images. It integrates these models into a smartphone app for real-time, on-site detection, improving accessibility and speed compared to prior manual or offline methods. This approach enables proactive monitoring of illegal wildlife trade in both physical and online markets.
Market Size (TAM)
$2–10B TAM, $1–2B SAM; assumption: global wildlife trade enforcement and monitoring market with growing regulatory focus.
Potential Customers & Pain Points
- Wildlife Conservation Agencies needing efficient trade monitoring
- Law Enforcement Agencies detecting illegal wildlife trade
- Customs and Border Control requiring quick product verification
- Online Marketplaces seeking to prevent illegal wildlife sales
- NGOs focused on wildlife protection needing scalable detection tools
Business Model
Subscription-based licensing for agencies and NGOs; custom integration services for enforcement bodies; potential freemium model for public awareness.
Competitive Landscape
- Wildlife Insights
- TRAFFIC
- iNaturalist
Implementation Challenges
- Data variability and image quality in real-world settings
- Adoption by law enforcement and customs agencies
- Legal and privacy concerns in monitoring online marketplaces
Validation Strategy
- Pilot deployment with select wildlife enforcement agencies
- Field testing in physical markets and border checkpoints
- User feedback collection and model refinement based on real-world use
Research Paper Overview
Detection of trade in products derived from threatened species using machine learning and a smartphone
Summary
This paper presents machine learning models that identify wildlife products from elephants, pangolins, and tigers in images to detect illegal trade. The models achieve up to 93.5% accuracy for tiger products and 84.2% overall. A smartphone app was developed for real-time identification, enabling law enforcement and authorities to monitor wildlife trade both online and in physical markets.