What question did this study set out to answer?

This research aims to evaluate the effectiveness of machine-learning tools in detecting illegal wildlife trade during real-world seizures.

February 28, 2026Open Access

Detect and Trace: An Australian Field Trial Using Machine-Learning Tools to Combat Illegal Wildlife Trade

Key Points

This research aims to evaluate the effectiveness of machine-learning tools in detecting illegal wildlife trade during real-world seizures.
Conducted a seven-month field trial in Australia involving real-time wildlife seizures.
Tested two machine-learning tools using CT X-ray and pXRF scanning technologies.
Scrutinized 116 intercepted animals from various reptile and crustacean species.
Analyzed data from automated detection algorithms and provenance models.
Automated detection identified smuggled wildlife 56% of the time with the most effective algorithm.
Captured 48 high-resolution 3D X-ray images that aided in wildlife identification.
Common blue-tongue lizards were less frequently wild-caught compared to shingleback lizards.
Post-trial, there was a noted reduction in the number of wildlife parcels exported via postal services.

Abstract

The illegal wildlife trade is a global problem that continues to harm individuals, wildlife populations, ecosystems, and humans at an increasing rate. While efforts are underway globally to address the issue through a coordinated approach, the testing of new technologies in real-world settings remains limited. Here, we present the outcomes of an opportunistic Australian trial that tested two machine-learning tools during real-world seizures, including associated radiation-exposure safety data. During the seven-month trial, 116 animals were intercepted, representing reptiles and crustacea across five Genera: Tiliqua, Egernia, Oedura, Chelodina, and Euastacus. Of the 18 seized consignments, totalling 48 parcels, scanned through the RTT®110 CT X-ray baggage scanner, automated AI detected smuggled wildlife 56% of the time using the most successful algorithm (AT.3), and captured 48 high-resolution 3D X-ray images, which allowed identification of concealed wildlife. In addition, 33 Tiliqua sp. were scanned using the Olympus Vanta pXRF and the data analysed using previously published machine-learning provenance models. Common blue-tongue lizards (Tiliqua scincoides) were less likely to be wild-caught than shingleback lizards (Tiliqua rugosa). Alongside expert statements, provenance results were provided to enforcement agencies. Following the trial, there was a significant reduction in the number of seized parcels being exported through postal pathways. This trial demonstrates the impact of integrating new technology to support intelligence-led enforcement processes and reduce wildlife trafficking.

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