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

Abstract

Illegal and unsustainable wildlife trade remains a major conservation challenge and is increasingly shifting to online marketplaces where monitoring is difficult. To address this gap, we used real‐world images from online platforms, enforcement seizures, and market inspections to develop a deep‐learning object detection framework to identify products derived from three highly traded species: elephant, pangolin, and tiger. Our dataset of 39,941 images included both wildlife products and visually similar nonwildlife items, reflecting real enforcement complexity. We used cross‐entropy and asymmetric focal loss functions to train Faster Region‐based convolutional neural network (Faster R‐CNN) models and compared species‐specific and multispecies training strategies. The best‐performing model, trained with cross‐entropy on all species combined, achieved an overall accuracy of 84.2%, with high performance for pangolin (93.6%) and tiger (90.3%) and moderate performance for elephant (71.1%). Although asymmetric focal loss increased recall, it substantially reduced precision. We deployed the optimized model in a prototype smartphone application, potentially usable in real‐world settings for monitoring online images. With an independent test set, the application achieved 91.3% accuracy and an F ‐score of 0.86. These results demonstrate the potential of object detection models to support scalable, real‐time monitoring of wildlife trade and enable the rapid triage of suspicious posts, regulatory compliance, and conservation decision‐making.