Detection of trade in products derived from threatened species using machine learning and a smartphone application

The study presents a deep‑learning object detection framework that identifies products derived from threatened species—elephant, pangolin, and tiger—using real‑world images from online marketplaces, enforcement seizures, and market inspections. Trained on 39,941 images, the best model achieved 84.2% overall accuracy, with 93.6% for pangolin, 90.3% for tiger, and 71.1% for elephant. The optimized model was deployed in a prototype smartphone app, which reached 91.3% accuracy and an F‑score of 0.86 on an independent test set, demonstrating its potential for real‑time monitoring of wildlife trade.

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• computer-vision